What is the boostUrCAreer project?

BoostUrCAreer project aims at implementing at Université Côte d’Azur and with the support from the European Commission and the Conseil Region Sud-Provence-Alpes-Côte d’Azur a multidisciplinary doctoral programme in e-health.

This programme wishes to attract to the French Riviera 15 early-stage researchers (ESRs) with the world highest academic, creative and innovative potentials and enhance their employability. The proposed programme will foster interdisciplinary, intersectoral and international experiences with the objective of contributing to creating a new generation of PhDs equipped for both academic and non-academic careers in e-health and inclined to the great research and innovation challenges of tomorrow. In line with the strategy of excellence, interdisciplinary and innovation pursued by Université Côte d'Azur, every doctoral project will have to associate two laboratories of the University and a foreign academic partner. BoostUrCAreer will thus provide a diversified education combining the most fundamental aspects of research with the practice of transfer toward the socio-economic world. This dual expertise represents a real added value for career development and is acquired thanks to specific trainings on practical and transferable skills and a six-month mobility abroad at an international research laboratory. In addition, a close follow-up by two academic supervisors in fundamental laboratories as well as by an academic tutor and a local non-academic mentor will ensure the quality of doctoral theses and further facilitate the ESRs’ integration to the workforce.

To this end, Université Côte d'Azur will launch 2 international call for proposal campaigns: the first one in May 2019 and the second one in January 2020 to attract high potential applicants. Prior to the launch of the call for proposal campaigns, a internal call for PhD topics was launched among the researchers working for Université Côte d'Azur or one of its members. After a rigourous selection, the University has selected 30 PhD topics from which the applicants will have to choose from (see list below). Once recruited, the ESRs will have 42 months to complete their PhD including the six-month mobility abroad.


APPLICATIONS ARE OPENED FROM JUNE 12th 2019 until AUGUST 12th, 2019!!!!!

Why should you apply?

A unique programme relying on the 3 "I" principle: interdisciplinary, intersectoral and international to guarantee scientific excellence and professional development...

Early-stage researchers involved in the BoostUrCAreer doctoral programme are offered to obtain a degree substantiated by an actual development and broadening of their research competences. The doctoral students will be provided with :

  • an excellent research environment composed of top institutions ;
  • attractive and selective working conditions. The BoostUrCAreer students will design their curriculum in collaboration with their supervisors, including the host organisations for their academic secondment. Their career plan will be assessed yearly by the students themselves, academics and mentors ;
  • interdisciplinary research options. Each doctoral project will be interdisciplinary as it will require two supervisors from two research fields. The common training for all students will foster opportunities for more cross-fertilisation between students and disciplines ;
  • exposure to non-academic employment sectors. Thanks to the mentoring programme and the classes taught by non-academics, the BoostUrCAreer students will be highly and regularly exposed to the industry and other relevant employment sectors ;
  • international networking. With two supervisors, each BoostUrCAreer student will have access to two research networks. They will also have a dedicated budget for participating to conferences and for the academic secondment, which must be abroad ;
  • training on transferable skills. The modules of the BoostUrCAreer common training focuses only on transferable skills (ethics, management, entrepreneurship, intellectual property rights (IPR), communication to name a few) ;
  • high-quality supervision and mentoring schemes. To secure enough time available for the students, no BoostUrCAreer supervisor will have more than two PhD students (full time) to supervise each year. The mentor scheme will provide the students with an individual, personalised, and regular follow-up of their career plans ;
  • support for the possible commercialisation of doctoral research work, and will use the alumni network as much as possible ;
  • provision to participate or organise events to disseminate and communicate their results.

... while providing excellent working conditions to attract high achieving applicants

The programme will also provide excellent working conditions to the ESRs:

  • Attractive salary of 2709,00 € (gross salary) per month, as well as, different allowances (between 815 and 1215 euros per month) ;
  • A legal working time is 37 hours per week, with a daily working duration that does not exceed 10 hours ;
  • Subsidized lunches and monthly pass for public transportation ;
  • A total amount of yearly vacations of 45 days ;
  • Paid sick leaves ;
  • Parental leaves following the birth/adoption of a child ;
  • Sick and parental leaves add up to the 42-month duration of the contract ;
  • In addition to their income, the doctoral candidates who have family obligations will receive an extra family allowance of €400 per month. Furthermore, they will benefit for each child of a monthly financial help from the French social security (calculations based on the household income and on the number of children under the age of 20) ;

BoostUrCAreer doctoral candidates will be hosted in one of the UCA’s members’ research laboratories. They will benefit from an intense and creative research environment. Almost all laboratories encompass engineers, university professors, and researchers from national research institutes, such as CNRS, INRIA, INSERM, INRA, CEA, IRD, and OCA who are sharing different views and approaches. The early-stage researchers will thus get access to many facets of academic life.

All BoostUrCAreer will be granted a priority access to the shared research infrastructures of UCA. This includes data management facilities at the Centre for Modelling, Simulation and Interactions, experimental platforms (such as the mutualized microscopes and spectrographs, animal facilities and the social sciences experimental facilities of the House of Humanities), etc. In addition, as researchers employed in a French institution, the doctoral candidates will have access to all national research infrastructures.

Researchers with disabilities will benefit from specific arrangements from their host laboratories for ensuring that their working conditions are properly adapted.

PhD Topics

Supervisors :

  1. Research Director François Brémond, INRIA (French National Institute for computer science and applied mathematics) & CoBTEK (Laboratory of Cognition Behaviour Technology),
  2. Doctor of Medecine Susanne Thümmler, Laboratory of Cognition Behaviour Technology & CRA of CHU-Lenval (Autism ressources Centre of the CHU-Lenval Children's Hospital of Nice).

International partners : Doctor Jean-Marc Odobez, IDIAP Research Institute, affiliated to the EPFL (Ecole polytechnique fédérale de Lausanne).

Presentation of the PhD topic : 

Deep Learning in computer vision, and in particular for Action Detection, is an effective solution for studying human behaviors of large population, and could be applied to children with autism. It allows capturing, in a non-intrusive and continuous way over time, behavioral patterns. Action detection from live video streams is an important task for monitoring patients, building robots for assisted living and other healthcare applications. Although several approaches, including Deep Convolutional Neural Networks (CNNs), have significantly improved performance on action classification, they still struggle to achieve precise spatio-temporal action localization in untrimmed video streams.

The PhD student involved in this project will design novel algorithms for detecting actions, taking advantage of the latest research in Deep Learning. These algorithms will be validated on various international video benchmarks and on a new video database on autism spectrum disorders (ASD) and be published in most prestigious conferences (e.g. CVPR). The early detection of ASD is a crucial issue because it makes it possible to set up intensive and early appropriate care management when certain developmental processes can still be modified.

The PhD candidate will spend 6 months within the Perception and Activity Understanding group, at the Idiap Research Institute (Switzerland), in order to strengthen his international research carrier. The Autism Resources Center, from University Children’s Hospital of Nice (CHU-Lenval) will be part of the project to bring its expertise on ASD and will provide the clinical environment. Nively, the industrial partner of the project, will contribute to the technology transfer and to the consolidation of a marketable solution.

The expected PhD student should have a master in Data Science, with experience in Computer Vision and Deep Learning.



    Supervisors :

    1. Professor Thomas Lamonerie, IBV (Institute of Biology Valrose),
    2. Doctor Michel Barlaud, i3S (Laboratory of Information and communication Science of Sophia Antipolis).

    International partners : Associate Professor & Doctor Elyanne M.Ratcliffe, Farncombe Family Digestive Health Research Institute, McMaster University.

    Presentation of the PhD topic :

    While susceptibilities to psychiatric diseases can be inherited, catalyzers of these susceptibilities, especially regarding anxiety and mood disorders, are stressful events, particularly those that happen early in life. Although it is clear that early life stress (ELS) is a catalyzer, causal mechanisms are not understood and predictive biomarkers to diagnose, stratify patients and prevent these diseases are lacking. The main reason to that is the difficulty to normalize data from patients with highly heterogeneous genetic background and trauma history. In addition, the complex composition of biological samples such as blood requires powerful analytical methods to highlight quantitative variations as well as advanced mathematical tools to identify reliable indicators. It is thus important to develop models of psychiatric diseases together with statistical methods applied to large sets of biological data to discover predictive or diagnostic parameters associated with these diseases.

    This PhD project aims at using AI to identify signatures of risk of psychiatric disorders such as chronic anxiety and depression, that could be directly useful for clinicians. The ability to use AI to process large biological data sets is a highly sought-after skill in academics and in the food and drugs industry.

    The project will use an early life stress paradigm in the mouse, which, as in humans, affects reward circuits and increases susceptibility to these diseases later in life. The student will conduct a time course high-throughput analysis of urine metabolites and microbiota along mice life, and behavior tests to have their psychiatric profiling. The two first years will be spent between Nice and Canada. The student will then use advanced mathematical tools, under the supervision of a lead expert in artificial intelligence, to process all the data. He will do so according to psychiatric profiles, in order to identify the biomarkers of these psychiatric diseases to diagnose, predict and ultimately prevent them.

    The PhD student will be supervised by two members of this consortium. These two members are chosen based on their capacity to guide the student on the biological (Thomas Lamonerie) and mathematical (Michel Barlaud) aspects of the project. Indeed, the main challenge will be to successfully extract meaningful biological information using advanced mathematical models. The two supervisors have a long experience of supervising PhD student and are already interacting on regular bases with the third member of the consortium Thierry Pourcher. The three locations of these partners are close by, thus greatly facilitating physical meeting. All partners have intensely interacted on this project and have been able to develop a shared vision that will be very useful as a guideline. The role of each member is clearly defined and major problems have been thought through.

    Supervisors :

    1. Professor Anne Vuillemin, LAMHESS (Laboratory of sport science),
    2. Doctor Gilles Maignant, RETINES lab.

    International partners : Senior Lecturer Audrey de Nazelle, Centre for Environmental Policy, Imperial College London

    Presentation of the PhD topic : 

    Air pollution, physical activity, road traffic injuries are important determinants of health that are affected by transportation patterns. Studies have demonstrated the potential for increased walking and cycling to benefit population health and the environment. The role of city planning and design in promoting population health is increasingly recognized as an essential and promising solution. To make such benefits apparent to decision makers and stakeholders, and further ensure success of such solutions, more work is needed in developing health impact modeling tools which address in a robust manner real world policies and conditions and integrate a variety of impacts.

    The ASTHAIR PhD project aims at developing health impact models of proposed urban changes which consider multiple impacts, including co-benefits and trade-offs, integrates advanced knowledge on current activity patterns and other baseline conditions, and includes a framework for effectively communicating findings as feedback to stakeholders to ensure successful implementation and uptake. The results of this work should provide innovative solutions to promote and develop active transport. Industrial partners will be involved in the project and are interested in potential transfer.

    The main steps of the projects are to develop an integrated health impact modeling framework to quantitatively assess impacts of planned policies on health through pathways such as physical activity and exposures to air pollution, greenspace and traffic injuries; and to design a smartphone app which will collect activity and self-assessed health data from users and provide in return feedback on outcomes such as physical activity, air pollution, traffic, meteorological data.

    This work will be done in collaboration with Audrey de Nazelle, Centre for Environmental Policy, Imperial College London, UK.


    Supervisors :

    1. Associate Professor Yannick Tillier, CEMEF (Centre For Material Forming) Mines ParisTech,
    2. Associate Professor Nathalie Brulat-Bouchard, University Côte d'Azur & CEMEF (Centre For Material Forming) Mines ParisTech.

    International partners : Professor & Doctor Ivo Krejci, Department of Preventive Dental Medicine and Primary Dental Care, University of Geneva.

    Presentation of the PhD topic :

    A dentist spends as much time fixing defective restorations as dealing with initial tooth decay lesions! This is mainly due to the volumetric contraction of dental composites during the polymerization process. Replacing defective dental fillings costs a lot for the society (about $ 5 billion per year in the United States).

    This project is part of a larger one that aims at designing and creating experimental and numerical tools that will be proposed to dental composite manufacturers for the development of longer lasting dental composites. The “BoostURTeeth” project is only focused on the numerical aspect. It aims at developing realistic multiscale 3D finite element models (FEM) in order to numerically evaluate the effects of filler contents and resin properties on their mechanical properties.

    The work program has been designed to be as fluid as possible, starting (i) with generating the microscale model to describe all heterogeneities and resin/filler interactions, then (ii) developing the failure model to describe how cracks propagate at the interface (CZM models are usually preferred), (iii) to finish with the macroscale model to study the interfacial stresses increasing between the composite and the tooth during curing.

    This doctoral project is highly interdisciplinary. Thus you will be supervised by two supervisors from two research fields (computation mechanics and dentistry) and will be hosted for a 6-month period in the Department of Preventive Dentistry and Primary Dental Care at the School of Dental Medicine of the University of Geneva. By choosing this project, you have the opportunity to give patients a better future with a better dental health!

    Expected profile :

    Degree: Engineering degree or MSc in Computational Mechanics or Numerical Analysis with excellent academic records.

    Skills: Computational Mechanics and applied mathematics with a strong knowledge of the finite element method and programming (C++) skills. Non-linear solid mechanics and in particular knowledge in damage and fracture mechanics would be appreciated.

    Supervisors :

    1. Professor Tarek Hamel, I3S (Laboratory of Information and communication Science of Sophia Antipolis),
    2. Doctor Andrew Comport, I3S (Laboratory of Information and communication Science of Sophia Antipolis),
    3. Professor Emma Redding, Dance Science Department, Trinity Laban Conservatoire of Music and Dance.

    International partners :

    Presentation of the PhD topic : 

    Capturing and tracking high-detail human motion in real-time is a hot research topic that is fundamental to a wide range of applications including e-health, sport performance analysis, human-robot interaction, augmented reality and many more. This multidisciplinary thesis aims to work across the domains of real-time computer vision, deep learning and bio-mechanics. The aim is to address the problem of acquiring the pose, shape, appearance, motion and dynamics (torques, forces and velocities) of humans in 3D using multi-camera environment in real-time. One of the major challenges in live motion capture is the problem of dense modelling of non-rigid scenes.

    The objective of this thesis will be to design an end-to-end approach such that the input to a training network will be the set of images from multiple cameras observing the scene. The output of the network will be the high detail 3D geometry and dynamics acting on the human body. To this end we aim to use RGB-D sensor consistency to train the network in an unsupervised manner such that all images transform correctly to every other image with minimal error. For the training phase we will use many sensors, however, the use of the network for reconstructing the bio-mechanics will use much fewer sensors (even potentially with a single sensor). Such a low-cost set-up with a single camera could be used by a medical (or sport) practitioner for diagnosis.

    We aim to train the system using large amounts of training data acquired in collaboration with our partners. In particular, this project is part of a collaboration between Google (USA), Youdome startup (Monaco), the Rosella Hightower dance school (Cannes, France), the CNRS-I3S/UCA laboratory (Sophia Antipolis, France) and the Trinity Laban Conservatoire of Music and Dance (London). The PhD will be supervised by Dr Andrew Comport, Professor Tarek Hamel and Dr Emma Redding. The two industrial partners Google and Youdome will also collaborate with the PhD student. Their participation attests a strong applicative interest in the domain and a high potential for future employability.

    The PhD candidate will carry out a 6 month stay with one or several of the project partners. The candidate will therefore need to have a technical background with experience in computer vision, machine learning and kinematics with a strong mathematical background and knowledge in C++, Python, Pytorch, Tensorflow, RGB-D sensors along with a strong capacity to write publications in English. Experience with GPU acceleration and real-time systems would also be of interest.

    Supervisors :

    1. Research Director Madalena Chaves, Biocore, INRIA (French National Institute for computer science and applied mathematics),
    2. Associate Researcher Jeremie Roux, IRCAN (Institute for Research on Cancer and Aging).

    International partners : Doctor Diego Oyarzun, School of Informatics, School of Biological Sciences, University of Edinburgh.

    Presentation of the PhD topic :

    Initiation of cell death is a critical cellular decision in tissue homeostasis and cancer emergence. However, substantial variability is observed in tumor cell populations, where a fraction of clonal cells commits to cell death while the other survives, contributing to the reduced efficacies of anticancer therapeutics. This PhD project is among the first to link high-content analyses from dynamic imaging and single-cell multi-omics, with state-of-the-art theoretical and computational methods to provide a global understanding of the origins of tumor cell heterogeneity in response to cancer drugs.

    Working at INRIA and CNRS labs, the PhD candidate will develop an interactive numerical simulation platform based on mathematical models of cell signaling pathways, including stochastic components which she/he will develop with our partner during a visiting internship in the Biomolecular Control Group at University of Edinburgh. The PhD candidate will acquire a combined expertise in predictive modeling of heterogeneous single-cell data and dynamical systems, which are the fundamental assets of future research in interdisciplinary projects in academia and pharmaceuticals.

    As such, the work expected from the PhD student will be as follow :

    • Year 1. The first part of the thesis will be based on the existing model of the apoptosis receptor pathway. The student will explore new reactions and feedback effects, and propose model improvements to better understand single cell data.
    • Year 2a. Investigate pathways for inter-cellular communication and develop a model to describe the effect of cell-to-cell exchanges in the single cell response to death drugs. This model may include both deterministic and stochastic terms, in the case of molecules present in low amounts.
    • Year 2b. To study and model cellular pathways from a stochastic point of view, and the integration of a specific pathway within the cellular environment, the student will spend six months at D. Oyarzun's group.
    • Year 3a. Construction and analysis of a mathematical model for cell-to-cell communication and main feedback loops for the apoptosis receptor pathway. Model predictions on cell synchronization and dynamical responses.
    • Year 3b. Comparison of model predictions with new experimental data from dynamic imaging and single-cell multi-omics and conclude on most significant reactions for targeting by anti-cancer drugs.

    Supervisors :

    1. Professor Sylvane Faure, LAPCOS (Laboratory of Anthropology and Clinical, Cognitive and Social and Psychology),
    2. Professor Serge Antonczak, ICN (Institute of Chemistry of Nice).

    International partners : Doctor Thanh Xuan Thi Nguyen, Danang International Institute of Technology, University of Danang.

    Presentation of the PhD topic:

    In this project, the PhD student will study the impact of olfactory and musical stimuli on participants' well-being and cognitive performances, considering individual experiences and culture as moderators. In line with the quest by consumers for naturalness and well-being, local (perfume industry in Grasse) or international companies have shown their interests in such aspects. The PhD student will thus perform double blind protocols with both subjective (questionnaires) and objective (blood pressure, heart rate, electrodermal response with the new technology of Cocolab Platform) measurements within a high collaborative framework associating psychologists and chemists of Côte d’Azur (France) and DaNang/HoChiMin (Vietnam) Universities. Therefore, under the supervision of Prof S. Faure, expert in cognitive and clinical neuropsychology and of Prof S. Antonczak, expert in the chemistry of aromas and perfumes, the PhD will have to:

    • propose enhanced protocols based on a revue of literature (model of multisensory integration, well-being and cognitive performance) ;
    • set up the experimental protocols for a Western population (physiological measurement with biopac®, emotional identification with FaceRader® of Noldus®, manage synchronization of odorant’s and music’s diffusion with TheObserverXT®, neuropsychological and psychometrics scales assessment) ;
    • replicate these studies to a non-Western population (Da Nang University, 6-months research stay) ;
    • value the results (publication, congress…) and search for new funding and partnerships ;

    To this end, the PhD student should have an academic and applied experience in human experimental research (cognitive psychology, cognitive science, neuroscience…). She/He will benefit from the experience of both LAPCOS and ICN laboratories and the involvement of Dr. X. Corveleyn and Dr. M. Adrian-Scotto as contributor to this project.

    Supervisors :

    1. Professor Lionel Fillatre, i3S (Laboratory of Information and communication Science of Sophia Antipolis),
    2. Professor Nicolas Glaichenhaus, IPMC (Molecular and Cellular Pharmacology Institute).

    International partners : Doctor Raquel Iniesta, Departement of Biostatistics and Health Informatics, King's College London.

    Presentation of the PhD topic : 

    Datasets in medicine routinely involve thousands of heterogeneous variables, including clinical, neuroimaging, genomic and proteomic measures. The analysis of these datasets is challenging, especially when the number of measurements exceeds the number of individuals. Deep learning methods have brought breakthroughs in many fields including image recognition, video and sound analyses among others.

    The DECISION PhD project aims to develop a novel clinical decision support system for diagnosis, prognosis and personalized treatment in the field of Psychiatry. It is worth noting that, in the European Union, more than 30% of people are affected each year by mental disorders.

    The PhD student will process datasets consisting of both biological and clinical variables with a convolutional neural network. Her/his main objectives will be to show that such a deep neural network can make a piecewise linear approximation of the data manifold and that it can exploit this approximation to predict a (clinical) score defined over this manifold. Deep learning architectures are known to act as black boxes. By studying the theoretical properties of a deep architecture for linearizing the data manifold, we expect to make the results explainable.

    This work will be done in collaboration with The King's College of London (UK).

    Candidates should have (or expect to achieve prior to August 2019) a MSc degree (or equivalent) in Applied Mathematics or Computer Science (or a related discipline). Applicants are expected to possess fundamental knowledge and skills in one or more of the following aspects: Machine learning, Deep learning, Statistical estimation/decision theory, Numerical optimization and Good programming skills.

    Supervisors :

    1. Professor Frederic Precioso, I3S (Laboratory of Information and communication Science of Sophia Antipolis),
    2. Professor Pascal Staccini RETINES Lab - CHU Nice (University Hospital of Nice).

    International partners : Professor Doctor Eduardo Alves Do Valle Junior, School of Electrical and Computer Engineering, University of Camerino.

    Presentation of the PhD topic :

    In the healthcare domain, datasets are geographically (hospital, practitionner’s office, patient him/herself) and timely distributed.

    Besides health data collected during medical events, new flows of data are originated from the patient himself (quantified-self) as a record of his/her behavior, environment, life style, etc.

    In recent years, we have seen an explosion of successful applications of deep learning in medical domain, including image analysis for diabetese retinopathy analysis, breast cancer detection, cardiological disease classification, Electronic health records analysis, Genomics analysis, etc.

    Deep networks designed for these tasks have millions and billions of parameters that require enormous resources in terms of annotated data, huge memory and disk storage space, and computer power to manage them.

    Although many open-source implementations leverage the performance of GPU programming, the resources required to learn the right settings for these architectures are considerable which are often not reachable for standard hospitals.

    In this PhD, we examine the problem of convergence in a deep network with billions of parameters using several thousand GPU threads distributed within several GPUs not sharing memory, which is usually the case if several machines are assembled to solve a very large problem using a very large network.

    •    Build a multi-task model to predict simultaneously on several medical objectives (next medical exam, duration of stay at the hospital, health state...).

    •    Deploy the proposed Multi-task model on a low power multi-GPU cluster

    •    Generality of the approach to other target tasks and other data.

    For instance use the proposed method to analyze hospital stay datasets coupled with standard biological results related to hospital stays in order to predict useful biomarkers for common pathologies according to biological patterns.

    The candidate should hold a Master degree, should have strong background in Machine Learning and have practiced Deep Learning. Any experience with medical data will be appreciated.



    Supervisors :

    1. Researcher Benjamin Mauroy, VADER center (Center for Virtual modeling of respiration) & LJAD (Jean Alexandre Dieudonné Laboratory of mathematics),
    2. Professor Lisa Giovannini-Chami, Pneumology Department, Lenval Hospital,
    3. Reseacher Angelos Mantzaflaris, Aromath (AlgebRa, geOmetry, Modeling and AlgorTHms), INRIA (French National Institute for computer science and applied mathematics).

    International partners : Professor Olivier Debeir, Brussels Polytechnic School.

    Presentation of the PhD topic : 

    Amongst the most frequent lung’s diseases, many induce a shrinking of the bronchi, typically asthma, COPD (“smoker disease”), bronchiolitis in babies, cystic fibrosis, etc. One of the goals of the therapies is to correct those constrictions in order to restore normal air flows within the lung. It is however very difficult to know where the constrictions occur as no direct information can be obtained from routine lung’s explorations. Consequently, many therapeutic responses, such as chest physiotherapy, are empirical and are difficult to validate.

    This interdisciplinary PhD thesis aims at giving a scientific basis to this empirical knowledge. The main goal is to build for the first time an artificial intelligence (AI) that will be able to relate data from routine exploration with the localisation of the constrictions. This thrilling project will start with the gathering of relevant medical data with the help of the Lenval Children Hospital (Nice). The data will then be processed in collaboration with INRIA (Sophia Antipolis) and LISA – IMAGE laboratory (Université Libre de Bruxelles) in order to be included into the numerical models of the biomechanics of the lung developed in JA Dieudonné laboratory (Nice). The numerical models allow to fully control the localisations of the constrictions, and to mimic the corresponding results of routine explorations. Once run for a wide range of obstructive scenarios, the numerical models predictions will be used to teach a well chosen machine learning algorithm. The machine learning step will be made in collaboration with ULB and INRIA. Once the training of the AI is complete, it will be confronted to real patients' data and validated with the help of Lenval Hospital (Nice).

    During this work, the successful candidate will have the exciting opportunities to work with researchers from different disciplines, to spend six months in ULB, and to potentially develop a partnership with Microsoft Health. She/he will acquire not only competences in managing a multifaceted project, but also a rich interdisciplinary background in modelling, physiology, biophysics, numerics and artificial intelligence. All these topics meet now high demand in both academics and industry.

    For this ambitious and ground breaking project, we are looking for an exceptional, enthusiastic and open-minded candidate with a Master degree in engineering, numerical physics, applied mathematics or data science. The candidate should also be highly motivated with interdisciplinary work and biomedical applications.

    Supervisors :

    1. Associate Professor Abderrahmane Habbal, LJAD (Jean Alexandre Dieudonné Laboratory of mathematics) & INRIA (French National Institute for computer science and applied mathematics),
    2. Research Director Ez-Zoubir AmriIBV (Institute of Biology Valrose).

    International partners : Professor Pierre-Emmanuel Jabin, Department of Mathematics, Center for Scientific Computation and Mathematical Modeling, University of Maryland.

    Presentation of the PhD topic :

    According to the WHO, on a global scale, 1.9 billion people were overweight in 2016, comprising 650 million people with obesity. This high prevalence of obesity represents a serious threat to human health and well-being.  People with obesity suffer from stigmatization and a severely compromised quality of life.  Obesity strongly clusters with other comorbidities, in particular type 2 diabetes, arterial hypertension, dyslipidemia and certain cancers.
    Obese state and its associated complications have emerged as the leading causes of death in Western countries, associated with estimated health care costs of 81 billion Euros per year in Europe only.

    DyATOT is an interdisciplinary project intended to develop mathematical modeling, analysis and simulation of excessive accumulation of fat mass responsible of the progression of obesity and the associated metabolic disorders. The main aim is to establish comprehensive and predictive tools thereby leading to the development of efficient therapies to prevent and/or cure obesity.

    The DyATOT program is expected to lead to the modeling of several nutritional, genetic and mechanical mediators responsible of the development of obesity in the functional crosstalk between white and brown adipose tissues. As obesity is considered world-wide pandemic and its incidence is increasing, there is a high potential for economic transfer of the gained expertise and findings, through industrial grants and start-up creation (see [1]).

    The candidate is required to have background skills in modeling with partial differential equations, scientific computing and in biology. The Ph.D. student will develop expertise in cellular and molecular biology, as well as in physiology of adipose tissue and global analysis (omics).

    DyATOT aims at a highly interdisciplinary training for Early Stage Researcher closing the gap of young researchers with knowledge in combining mathematics and biology to improve metabolic health.

    The research plan will be as follows (in months) :

    •   Work with mice/omics in order to generate data (Institute of Biology  Valrose at Nice) M1- M24 ;
    •   Develop mathematical models : build appropriate PDE systems accounting for the white to brown adipocyte transition (Acumes team at Inria Sophia Antipolis) M1-M42 ;
    •   Secondment at University of Maryland in order to advance mathematical analysis of the developed models : M24-M30.

    The data generated will be analyzed in a comprehensive manner with up-to-date machine learning methods in order to develop a deep mathematical analysis of white to brown adipocyte transition.

    The two Host institutions offer a stimulating scientific environment with access to state-of-the-art technologies in an ideal context for a successful experience. The PhD student is expected to participate to the lab seminars, to international conferences and to the development of a mathematical obesity research network. 


    Supervisors :

    1. Professor Jerome Golebiowski, ICN (Institute of Chemistry of Nice),
    2. Doctor Renaud David, Memory Resource and Research Center, Memory Resource and Research Centre, CHU Nice (University Hospital of Nice), Research Centre Edmond & Lily SAFRA , CoBTeK (Cognition Behaviour Technology laboratory).

    International partners : Adjunct Professor Joel D.Mainland, Monell Chemical Senses Center, University of Pennsylvania.

    Presentation of the PhD topic : 

    Can we learn a computer how to smell or feel relaxed upon smelling? What is the impact of smelling on our mood and motivation? The PhD project aims to use machine learning and molecular modeling on properties measured through sensory analysis and psychophysiology experiments on human individuals. The goal is to design odorants to fight depression and anxiety using non-pharmacological approaches.

    This research topic gathers two very exciting fields, i.e. numerical modeling and the measure of emotions around the sense of smell. It is associated with the proximity of the city of Grasse (the world capital of perfumes) and the technopole of Sophia-Antipolis, where numerical approaches are central. The research will be supervised by world-experts in chemosensory science and psychiatry. Pr. Golebiowski’s group is a world leader in the numerical modeling of smell and taste ( and Dr. David’s group is a world leader in psychiatry related to autonomy ( They both published reference articles in their fields.

    The candidate will mostly build numerical models to connect chemical structures to their effect on emotion and motivation on one part and on the odorant receptors on the other part. She/He will also partly oversee experiments because it is always better to master the data one tries to model. As such, the PhD candidate work will be divided as follows:

    1. bibliography and building of a database gathering odorants and their effect on physiological parameters as well as on biological receptors: at Nice and at Monell, Philadelphia, USA ;
    2. psychophysiology data acquisition on control panel (at Nice and with the company Expression Parfumées, Grasse) as well as on elderly patients at Nice ;
    3. numerical modelling to build structure-emotion/motivation relationships. Correlation between odorants receptors activation and emotion/motivation effects ;
    4. confirmation by in vitro assays of the role of odorant receptors in mood regulation (at Nice and at Monell, Philadelphia, USA).

    The candidate should be familiar with molecular or numerical modeling, or familiar with chemical senses. She/he should have a master’s degree in physical chemistry, cheminformatics, or data science.

    Supervisors :

    1. Research Director Xavier Descombes, INRIA (French National Institute for computer science and applied mathematics) & I3S (Laboratory of Information and communication Science of Sophia Antipolis),
    2. Research Director Ellen Van Obberghen-Schilling, IBV (Institute of Biology Valrose).

    International partners : Professor Alin Achim, Department of Electrical & Electronic Engineering, University of Bristol.

    Presentation of the PhD topic :

    Pathological extracellular matrix (ECM) of tumor tissue contributes to the progression and spead of cancer. This is the case for head and neck cancer, the 6th most prevalent cancer worldwide. Immune-based therapies have shown promising results, yet only a fraction of patients responds. This project aims at better characterizing the ECM and its regulation of immune escape mechanisms using in vitro models developped at iBV and multi-parametric histological stainings of ECM components in human head and neck tumors.

    The objectives of the project are twofold. First, the PhD student will develop a machine learning framework to characterize and classify the different types of ECM in healthy and pathological contexts from slide scanner data acquired at iBV (Nice, France). Secondly he/she will propose a model based on graphs of the ECM and derive the statistical tool to simulate and analyse the ECM.  The numerical and mathematical development will be performed in the Morpheme Team (Sophia Antipolis, France). The statistical framework will be developed in collaboration with Bristol University during a six-month stay.

    The candidate will have a master in computational science, applied mathematics, bioinformatics or a related filed. She/he will have some skill in Pyhton and/or Matlab programming.

    Some backgroung in biology will be a plus.

    Supervisors :

    1. Researcher, Marco Lorenzi, INRIA (French National Institute for computer science and applied mathematics),
    2. Research Director Barbara Bardoni, IPMC (Molecular and Cellular Pharmacology Institute).

    International partners : Doctor Andre Altmann, Centre for Medical Image Computing, University College London.

    Presentation of the PhD topic :

    This project envisions a novel paradigm for machine learning in healthcare based on the innovative concept of federated learning. Our goal is to exploit the power of modern learning methods at full capacity within the current clinical data scenario. To this end, we will focus on methodological, technical, and translational advances towards the development of a novel generation of federated learning methods for the analysis of private and large-scale multi-centric biomedical data.

    This project will provide the fellow with highly competitive skills for securing a position in the tech industry, in particular in startup and companies in the domain of machine learning and artificial intelligence. Furthermore, the strong biomedical application tackled during the PhD project will allow the student to acquire solid competences in biomedical data management and analysis. This aspect may open up important career perspectives in the field of biotech, pharmaceutical, and clinical research.

    The project will count on the expertise and collaboration of the partners of the ENIGMA consortium, a worldwide network of clinical centers providing data and expertise in dementia research. The project will also involve a 6 months visit period to the Centre of Medical Image Computing (CMIC) of University College London (UCL).

    During the project the candidate will :

    • Develop learning methods for federated analysis for private and distributed data ;
    • Gather knowledge in advanced statistical learning methods - Bayesian learning, Kernel methods, non-parametric learning, variational inference ;
    • Develop and deploy algorithms in several context, with special focus in biomedical and clinical application ;
    • Acquire skills in the advanced processing of medical images and sensors data ;
    • Collect/investigate datasets containing several modalities, such as brain images and genetics data ;
    • Interact with INRIA and CNRS students and researchers, and participate to scientific life of the institutes.

    Required competences:

    Statistics, optimization, and mathematical modeling are essential (Master 2 level). Knowledge in signal processing desired. Solid programming and IT skills are necessary (Python, bash, version control systems), along with strong communication abilities.


    Supervisors :

    1. Professor Claire Migliaccio, LEAT (Laboratory of Electronics, Antennas and Telecommunications),
    2. Associate Professor Victorita Doelan, LJAD (Jean Alexandre Dieudonné Laboratory of mathematics).

    International partners: Peter Barrowclough, Lincoln Agritech Ltd (New Zealand).

    Presentation of the PhD topic:

    Microwave imaging (MI) has attracted significant research interest in recent times. By exposing tissues to low-level microwave incident field and capturing the scattered field by an array of antennas, the estimation of the dielectric properties of the tissues can be approximated and a diagnosis inferred.  There is still an intractable conflict when applying current microwave approaches to non-contact medical scanning to attain sufficient resolution and penetration.

    The idea of the project lies in a challenge to design a microwave lens for obtaining a super spatial resolution based on the evanescent microscopy for developing a novel, non-contact, hand-held medical imaging scanner (MIS) that delivers high resolution imaging for use by healthcare practitioners.

    The candidate will model of the scanner as well as develop the reconstruction algorithm based on open source FEM codes and participate to the trials of the whole system. The Ph.D subject concerns the domain of applied mathematics and scientific computing for medical applications.

    The project will be developed in close cooperation with Lincoln Agritech, New Zealand, an independent R&D provider to the private sector and government and hospital of Nice.

    The Ph.D subject aims to develop a new branch of medical imaging. The Ph.D will be among the first researchers to able to work in this new branch. Her/his expertise will be therefore sought by professionals.

    Supervisors :

    1. Professor Michel Riveill, I3S (Laboratory of Information and communication science of Sophia Antipolis),
    2. Research Director Silvia Bottini, MDLab (Medical Data Laboratory), University Côte d'Azur,
    3. Professor Véronique Paquis, IRCAN (Institute for Research on Cancer and Aging).

    International partners :

    • MyDataModels (France),
    • Doctor Claudio Donati, Computational Biology Unit of the Research and Innovation Centre, Fondazione Edmund Mach (Italie).

    Presentation of the PhD topic :

    Mitochondrial diseases (MD) are rare disorders caused by deficiency of the mitochondrial respiratory chain, which provides energy in each cell. MD are caused by alterations (variants) on genes involved in mitochondrial functions. The diagnosis of MD is based on the identification of the disease responsible gene(s), that will allow to be able to offer genetic counseling, prenatal diagnosis, to consider therapeutic approaches and to improve the care of patients. Nowadays, technologies currently used for detecting causal variants is far from complete, ranging from 25 to 50%.

    To address these needs our research teams propose to gather three different domains: medical, bioinformatic and machine learning, in order to set up an integrated multi-omics approach to identify novel causal variants. We foresee that this project will contribute to set up new diagnostic tools to reduce the number of patients with a diagnostic stalemate. This study will settle the milestones to transfer the conjoint use of multi-omics technologies from research fields to diagnostic environment.

    The project is mainly composed by three steps, specifically the candidate will :

    1. perform bioinformatic analysis of multi-omics data ;
    2. develop a deep-learning multi-integromics approach ;
    3. implement a new variants prioritization AI algorithm.

    This project will allow to develop novel algorithms that will found application not only in MD diagnostic, but also in other genetic disorders and cancer, to allow the development of personalized medicine to ameliorate patients healthcare. We foresee that this project will provide a product easily transferable to non-academic field and easily employed in medical environment and several industrial sectors.

    Importantly, the fellow will gain outstanding competences in data science, an exponentially growing field in high demand in any field within and outside academia. In support of that, the intervention of the company “MyDataModels” in the current project will facilitate and enhance the integration of the fellows into non academic environment.

    Supervisors :

    1. Professor Raphael Zory, LAMHESS (Laboratory of Human Motricity, Expertise, Sport and Health),
    2. Researcher Laurent Busé, Aromath (AlgebRa, geOmetry, Modeling and AlgorTHms), INRIA (French National Institute for computer science and applied mathematics).

    International partners : Associate professor Katia Turcot, Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Laval University.

    Presentation of the PhD topic :

    In France, the expenses in physical rehabilitation increased from 7.3 to 8.4 B€ between 2010 and 2015, mainly due to the ageing population, the increase of chronic pathologies such as strokes or Parkinson, and the shortening of the hospitalization time. 70% of the activity of rehabilitation institutions is about gait (first step for the regain of autonomy). Accurate reliable knowledge of gait characteristics at a given time, and even more importantly, monitoring and evaluating them over time, may enable early diagnosis of diseases and their complications and help to find the best treatment. Three-dimensional motion analysis is the gold standard for clinical gait analysis (CGA), particularly in the presence of pathologies that affect walking. Today, less than 1% of the patients benefit from CGA.

    The main objective of this project is to develop a method based on an innovative low-cost motion analysis system allowing an accurate quantification of gait deviation parameters during functional tests, including spatiotemporal and full-body kinematic parameters. For that purpose, the recruited student will design novel parametric continuous models providing good representations of walking, with the goal to obtain reliable and robust approximations of all possible walking patterns from noisy point sets obtained via 3D camera acquisitions. By combining techniques from geometric modeling and machine learning adapted to our context, he will devise new fitting algorithms adapted to these models to identify the best instance for a wide range of data sets. He will also participate to the acquisition of medical data (3D CGA) which are mandatory to successfully create and validate the models, and to improve the general performance.

    The student involved in this project will benefit from academic expertise and training in the complementary fields of biomechanics, mathematics and computer science. He will be supervised by Raphael Zory who leads the team “Motor deficiencies and physical activity” on the LAMHESS and by Laurent Busé, researcher at Inria Sophia Antipolis – Méditerranée and specialist on algebraic methods and representations for complex shapes. The student will also get experience in technology transfer as this project will be conducted in collaboration with the Ekinnox company. Candidates should have appropriate academic qualifications in Computer Science, Mathematics or biomechanics (motion analysis) and strong background in programming.

    Supervisors :

    1. Professor Olivier Meste, I3S (Laboratory of Information and communication Science of Sophia Antipolis),
    2. Medical Doctor Marie-Noële Magnié-Mauro, Neuroscience Department, CHU Nice (University Hospital of Nice).

    International partners : Associate Professor Roberto Sassi, Biomedical Image and Signal Processing Laboratory, University of Milan.

    Presentation of the PhD topic : 

    The aim of this PhD thesis project is to improve, through interdisciplinary collaboration, our knowledge on the role of the cerebellum, especially in its functional asymmetries during cognitive, proprioceptive and motor processes. Multimodal functional explorations will be carried out through ECeG (ElectroCerebellarGrams) recordings coupled to f-MRI (functional Magnetic Resonance Imaging) or f-NIRS (functional Near Infrared Spectroscopy), concurrently with the development of ad-hoc signal processing methods. Experiments in neuro-psychology will build upon interactive devices (as our current patent-pending tablet-based EEG coupled application) to fine tune the study and detection of cognitive and motor skills issues in different targeted populations (children with cognitive or motor particularities, patients with cerebellar syndromes …). With a motivation towards m-health (mobile e-health), this project will develop novel ways to help in the process of recovery/improvement by investigating synergies in complementary techniques like neuro-feedback/neuro-training with alternative interactive devices like Virtual Reality helmets or Wii-like motion sensitive remote sensors coupled with EEG/ECeG/f-NIRS recordings.

    The PhD student will be primarily located at I3S, co-supervised by researchers from two UCA labs (I3S and BCL) and trained through multiple internships in major European and North-American first class research centers with whom the two groups have close collaborations. He/she will have extended access to our EEG experimental room at CHU Pasteur. The Biomedical Signal Processing group (Signal) at I3S, the Neuro-psychology group at CHU/BCL and researchers from the Sports Science department will provide full support to the PhD student in this exploration of functional cerebellar particularities.

    To apply to this project, the PhD student should have a master degree in electrical engineering or computer science with good knowledge in signal processing and data processing. He/she should have displayed good practical programming skills and have some fair knowledge in electronic. A genuine interest in neuro-psychology and biomedical engineering is welcome.

    The acquired knowledge and practical expertise in the domain of brain sensors, neuro-recordings and imaging techniques, his/her implication in the development of android-based tablet applications coupled with medical devices should be great assets for the PhD student to easily find an interesting position either in medical/imaging companies, academia or even research hospitals after the PhD.

    Supervisors :

    1. Research Director Michèle Studer, IBV (Institute of Biology Valrose),
    2. Associate Professor Franck Grammont, LJAD (Jean Alexandre Dieudonné Laboratory of mathematics).

    International partners : Senior Researcher Luca Berdondini, NetS3 Laboratory (Microtechnology for Neuroelectronics), Istituto Italiano di Tecnologia (Italie).

    Presentation of the PhD topic : 

    A major challenge in the study of the nervous system, either normal or pathological, is to understand how complex brain functions are implemented and executed at the neural circuit level. We propose to use High Density MultiElectrode Array (HD-MEA) technology both on ex vivo and in vivo preparations to record the activity of thousands of neurons and apply innovative computational techniques to analyze how neurons modulate and synchronize their activity within neuronal circuits. The results of this work should provide innovative solutions to develop new implants for cerebral or medullar stimulation in humans and be of great interest both for biotechnological industry and medicine.

    Major activities: The candidate must have accomplished his main education in Neuroscience, but some complements in the engineering and/or math-info domains will be very appreciated. During the first year, the candidate will be trained in the experimental use of high-resolution electrophysiology instrumentation at IIT, and in basic molecular and morphological biological techniques at iBV. Since the second year, this knowhow will allow the implementation of experimental studies aimed at functionally and morphologically profiling brain circuit development in healthy and pathological animal models. During the third year, the candidate will implement the analysis of the acquired data using computational and statistical methods that will be developed at LJAD since the first years of the project.

    Supervisors :

    1. Researcher Carole Rovere, IPMC (Molecular and Cellular Pharmacology Institute),
    2. Researcher Eric Debreuve, I3S (Laboratory of Information and communication Science of Sophia Antipolis).

    International partners : Research Director Denis Richard, IUCPQ-UL (Québec Heart and Lung Institute), Laval University.

    Presentation of the PhD topic : 

    Obesity and metabolic syndromes correspond to a state of chronic systemic inflammation that leads to deregulation of feeding behavior. Cell morphometric tools are becoming useful tools for studies associating cellular responses in the brain with feeding behavior.

    Thanks to an innovative technological approach, this project aims to understand the cell based mechanisms involved in the cerebral inflammatory response induced by different types of fat diets. The candidate will develop an image analysis procedure to automatically, i.e. a reliable and investigator independent procedure, measure the changes in the morphology of astrocyte and microglial cells to determine the degree of cell activation by fat diets.

    This objective will be decomposed into three main steps:

    1. development of specific image processing tools and pipelines to automatically detect glial cells on images
    2. characterization of these detected objects, and
    3. analysis of these data using machine learning.

    These tools will be developed to be interfaced with an OMERO (The Open Microscopy Environment) database already available for all the teams in the life sciences institutes in UCA and all their potential collaborators worldwide.

    We will then attempt to define, using pharmacogenetic tools, whether inhibition of early postprandial activation of glial cells prevents food intake and obesity in order to be able to offer innovative therapeutic management for the treatment of obesity.

    The candidate should have an applied mathematics or signal processing background with knowledge of, or strong interest in biology. The candidate’s profile would be someone able to adapt and develop new methods for extracting and analyzing tree-like structures, to understand some biology (or eager to learn), and then to transfer some methodological knowledge and tools to biologists.

    Supervisors :

    1. Researcher Isabelle Mus-Veteau, IPMC (Molecular and Cellular Pharmacology Institute),
    2. Associate Professor Stéphane Azoulay, ICN (Institute of Chemistry of Nice).

    International partners : Associate Professor Paolo Ruggerone,  Department of Physics, University of Cagliari (Italie).

    Presentation of the PhD topic : 

    Cancer drug resistance is a major problem of chemotherapy nowadays. Our team recently identified the Hedgehog receptor Patched as a drug efflux pump that participates to the resistance of cancer cells to chemotherapy. Thanks to a screening program, Panicein A hydroquinone (PAH), a natural compound purified from a marine sponge, was identified as an inhibitor of drug efflux activity of Patched. The synthesis of PAH allowed us to confirm that PAH increases the cytotoxic effect of several chemotherapeutic agents on melanoma cell lines in vitro and in vivo. The use of PAH in combination with chemotherapy may be a novel and innovative way to circumvent drug resistance, recurrence and metastasis of tumors.

    To get further comprehension of the mechanism of action and synthesize a more potent compound, the PhD student will have to :

    • optimize the lead molecule PAH thanks to a combination of in silico modelisation and structure-activity relationship (SAR) studies (docking of PAH on Patched structure and drug design to propose PAH modifications, synthesis of PAH analogues, effect of each analogues on the cytotoxicity of a chemotherapeutic agent such as vemurafenib on melanoma cells and IC50 determination) ;
    • provide proof-of-concept of the efficacy of the best optimized leads on melanoma but also on more Patched-expressing cancer cells (effect of the best PAH analogues on the proapoptotic, anticlonogenic and antiproliferatif effects of vemurafenib on melanoma cells in culture, and on the cytotoxicity of other chemotherapeutic agents on other cancer cell lines in culture).

    The final objective is to obtain a clinical candidate that could be considered for clinical testing with a Pharma partner.

    This project will be supervised by Dr. S. Azoulay (Institut de Chimie de Nice, France), for the chemical part, and by Dr. I. Mus-Veteau (Institut de Pharmacologie Moléculaire et Cellulaire, Nice, France) for the biological part.

    The applicant must have a solid background in organic chemistry and notions of cell biology. In silico notions will be appreciated since he/she will have to perform a 6-month internship in the laboratory of Pr. P. Ruggerone at the University of Cagliari in Italy to carry out computational studies (docking and drug design) allowing to guide the synthesis of new and more effective analogues of PAH.

    A thesis in medicinal chemistry, in silico experiments, and molecular tests on cancer cells will allow the student to get skills from drug design to cell assays that will be highly marketable within pharmaceutical industry and academia.

    Supervisors :

    1. Researcher Gianni Liti, IRCAN (Institute for Research on Cancer and Aging),
    2. Researcher Agnese Seminara, INPHYNI (Nice Institute of Physics).

    International partners : Associate Professor Marco Cosentino Lagomarsino, Physics Department, University of Milan.

    Presentation of the PhD topic : 

    The emergence of drug resistance is a major health problem that can thwart therapeutic control of a wide spectrum of diseases, from bacterial and viral infections to cancer. Drug resistance are regulated by multiple interacting quantitative trait loci (QTLs) as well as by novel mutations that evolve during the treatment process. Dissecting the genetic mechanisms underlying this phenotypic variation is a major challenge and this problematic apply to many human genetic diseases. Indeed, despite decades of genome wide association studies (GWAS), the genetic variants identified only explain a small fraction of the trait heritability, leaving the open question on whether accurate complex trait prediction can be achieved.

    The PRELUDE project aims to understand how drug resistance arises and evolves using bacteria and yeast as genetic systems. To do so, the interdisciplinarity and the experimental and computational approaches using sequencing and large-scale genomic analysis make the project state-of-the-art, and will open endless possibilities in both the academic and the private sector.

    Within this project, the PhD student will build pangenome datasets from large cohorts of bacterial and yeast collections and explore the emerging pangenome graph paradigm. Second, she/he will acquire phenotype data in spatially structured environments to map genetic determinants involved in drug resistance. Finally, the data will be integrated in a cohesive theoretical framework to generate predictive models.

    We are seeking a PhD candidate in the fields of health-related evolutionary genomics. The ideal candidate has knowledge of biology (evolutionary biology/genetics/genomics) and/or quantitative sciences (physics/mathematics/bioinformatics). The combined scientific backgrounds of the PIs (genetics/theoretical physics), will ensure advanced training on both sides. A possible secondment within a non-academic partner ( provides a direct link with a modern biotech industry.

    The Liti’s lab works at the forefront of the fields of genetics and genomics and has pioneered different approaches for powerful decompositions of phenotypic variation. The Seminara’s lab has developed a state-of-the-art phenotyping platform that will be crucial to understand the evolution of drug resistance as a function of varying concentrations and environments. The international partner Cosentino-Lagomarsino is a theoretician with a track-record in model development and model-guided data analysis in biology and works within a world leading medical institute (

    Supervisors :

    1. Research Scientist Maxime Sermesant, INRIA (French National Institute for computer science and applied mathematics),
    2. Assistant Professor & Doctor Pamela Moceri, CHU Nice (University Hospital of Nice).

    International partners : Research Professor Bart Bijnens, IDIBAPS (Biomedical research Institute August Pi I Sunyer), ICREA (Catalan Institution for Research and Advanced Studies).

    Presentation of the PhD topic : 

    Despite AI important success in the recent years, its limited robustness to variations in input data makes it challenging to apply in healthcare. One reason is the lack of prior knowledge on human anatomy and physiology. Biophysical modelling is a principled mathematical framework to describe physiology which can encode prior medical knowledge. Electromechanical modelling of the heart has been an active research area in the last decades, however most of the focus has been on the left ventricle, while the right ventricle has been mostly ignored. Right ventricular (RV) function evaluation is of utmost importance in heart failure, congenital heart disease, pulmonary arterial hypertension, pulmonary embolism, and most of respiratory diseases.

    This project is at the frontier between applied mathematics, computer science and cardiology. Over the last 20 years, Dr. Maxime Sermesant at Inria has developed state-of-the-art mathematical models of the myocardium, as well as methods to personalise such models to clinical data for diagnosis and therapy planning. At Nice University Hospital, Dr. Pamela Moceri has developed an expertise in the clinical evaluation of the right ventricle, with state-of-the-art tools for detailed analysis of the RV shape and function and numerous clinical publications.

    This project is international with a secondment at UPF in Barcelona with Pr. Bart Bijnens, a renowned researcher in cardiac echography and physiology, with a special interest in the right ventricle. UPF developed a detailed model of the RV fibrous structure based on synchrotron imaging, which impact on simulations started to be explored. It is also in collaboration with the company Philips Healthcare in Paris through Dr. Mathieu de Craene, doing research on the analysis of cardiac shape and motion. It will enable privileged access to state of the art commercial tools. Interactions with industrial researchers will demonstrate how the tools developed could be integrated in future products.

    Healthcare and biomedical engineering have one of the strongest recruitment increase in the last years, and skills acquired through this project will position well the fellow for his career. This project will utilise computational approaches in healthcare, which is a research area with an important growth. The interactions with academic and industrial partners will ensure employability in these two sectors. Medical imaging companies are currently developing new tools for shape and deformation analysis of the right ventricle. Such modelling approach is very complementary and could extend the possibilities of such products. Therefore there is an important potential for technology transfer. Finally, the Digital Twin concept which aims at creating a digital version of a patient to help diagnosis and therapy planning is currently promoted by large healthcare companies (Philips, Siemens,...). This electromechanical modelling project is perfectly in line with this concept, and should be of interest to these companies.

    Work plan:
    The project will follow a natural evolution of mathematical modelling of the right ventricle, starting from the shape and structure then moving to electrophysiological and biomechanical modelling. This will be achieved in conjunction with the analysis of the corresponding clinical data available. In the later stages of the PhD, this will be applied to selected pathologies. Here is an outline of the PhD timeline:

    • Year 1

    1. RV shape (6 PM): statistical shape analysis of the RV to build a template mesh
    2. RV structure (3PM + 3 PM Secondment): data analysis and model for a template fibre architecture

    • Year 2

    3. RV electrical activation (6PM): statistical analysis of activation maps for template electrophysiology
    4. RV deformation (6PM): statistical analysis of RV strain to adjust biomechanical model

    • Year 3

    5. RV mechanical contraction (6PM): contractile RV function estimation for personalised simulations
    6. RV pathologies: mechanisms and predictions (3PM + 3PM Secondment): selection of pathologies where clinical data enable personalised simulations

    Supervisors :

    1. Research Director Robert Arkowitz, IBV (Institute of Biology Valrose),
    2. Research Director Laure Blanc Feraud, I3S (Laboratory of Information and communication Science of Sophia Antipolis).

    International partners :

    • Professor Neil A. R. Gow, University of Exeter,
    • Professor Michael Unser, Biomedical Imaging Group, EPFL (Ecole polytechnique fédérale de Lausanne).

    Presentation of the PhD topic : 

    Worldwide, fungal infections cause significant morbidity and mortality and Candida species are major etiological agents of such life-threatening infections. Candida albicans, a normally harmless commensal, is found on mucosal surfaces in most healthy individuals, yet it can cause superficial as well as life-threatening systemic infections. Its ability to switch from an ovoid to a filamentous form, in response to environmental cues, is critical for its pathogenicity. The apical zone of the filament is densely packed with multiple highly dynamic membrane compartments, including secretory vesicles and Golgi cisternae. To understand the exquisite regulation of apical polarized growth, it is critical to follow the movement of these compartments in 3D, with high spatial and temporal resolution. This project will develop, optimize and apply super-resolution imaging approaches, in particular those taking advantage of fluorescent molecule blinking and their independent fluctuations in time, to study membrane traffic reorganization during filamentous growth in this Human fungal pathogen.

    Candidates should have either a strong math/computational (convex/nonconvex sparse optimization in image processing, time series deconvolution, super-resolution) or a strong biological/microbiological (microscopy, mycology) background and be motivated to work in an interdisciplinary environment, with the possibility of short stays in life science biotechnology companies.

    The recruited PhD student will follow different fluorescent protein fusions expressed in C. albicans live cells in super-resolved images obtained by reconstruction from wide-field acquisition. The entire acquisition pipeline will be optimized, from the experimental conditions to the reconstruction algorithm, for quantitative analysis of C. albicans hyphal, subcellular structure and dynamics.

    The supervisors have extensive experience in image processing and reconstruction (L. Blanc-Féraud) and fungal cell biology (R. Arkowitz) and S. Schaub has developed a super-resolution microscope taking advantage of multiple-angle total internal reflection fluorescence.

    Supervisors :

    1. Researcher Maria Duca, ICN (Institute of Chemistry of Nice),
    2. Professor Véronique Michelet, ICN (Institute of Chemistry of Nice).

    International partners : Doctor Roger Estrada Tejedor, IQS (Sarria Institute of chemistry) School of Engineering, University Ramon Llull.

    Presentation of the PhD topic : 

    One of the most amazing discoveries of the past decades in the domain of genetic oncology is that cancer is related to alterations of both protein coding genes and non-coding RNAs, such as microRNAs (miRNAs). The purpose of this project is the development of novel small-molecule drugs targeting specific oncogenic miRNAs production via original catalytic and green methodologies according to Diversity Oriented Synthesis.

    To do so, the PhD student will conduct three concomitant tasks :

    • Task 1: Synthesis of small molecules via original methodologies according to Diversity Orientated ;
    • Task 2: Evaluation of the biological activity of the synthesized compounds on oncogenic miRNAs involved in gastric cancers, glioblastoma and colon cancer ;
    • Task 3: Molecular modelling studies.

    The project will be developed thanks to the interdisciplinary combination of organic chemistry, biochemistry, biophysics and computational studies. The project and the PhD candidate will benefit of the interdisciplinary activities of the two supervisors (Dr. M. Duca & Pr. V. Michelet) and of the expertise in computational studies of the international collaborator (Pr. R. Estrada Tejedor). This is a highly challenging and very promising approach that would open the way for innovative targeted cancer therapy and will therefore guarantee employability in R&D companies or universities.

    How to apply?

    Application Procedure

    Deadline to submit: 12/08/2019 à 05:00 PM UTC+1 (CET)

    Convinced? if so, please follow the procedure described below to apply to one of our PhD topics :

    1. Identify the PhD topic you wish to apply too from the list above
    2. Read carefully the Applicant guide
    3. Download and fill out the Application form collecting personal information (name, address, country of residence, place(s) of activity/place(s) of residence in the 5 years previous to the deadline), basic, synthetic and factual details on your training and skills, on their academic and non-academic experience, on the envisioned supervising team, host laboratory and research project.
    4. Create your online CRU account to get access to the online application portal
    5. Connect to your CRU account and upload all requested documents
    6. Submit your application

    Don't forget to upload all the requested documents : 

    • a cover letter describing your motivations and professional project
    • a curriculum vitae
    • an abstract for the project you are applying for
    • grade transcripts for your Bachelor’s and Master’s degrees
    • Bachelor’s and Master’s diplomas
    • Scientific production (if any)
    • Contact information of 2 references

    ** All documents must be sent in English or an English translation has to be provided.

    All the documents required in the checklist must be submitted, otherwise, the file is considered incomplete and your application will be rejected.

    Apply now

    Deadline to submit: 12/08/2019 à 05:00 PM UTC+1 (CET)