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.




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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: doctoral candidates will get 2709 € as living allowance, including employer cost (gross salary ~ 1900 €, netto salary ~ 1500 €). In addition, the fellows will get travel and mobility allowances (815 € per month, not taxable) ;
  • 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 Olivier Humbert, Department of Medical imaging and nuclear medicine (Centre Antoine Lacassagne).

    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/sparse learning (Olivier Humbert) 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. Researcher Pierre Kornprobst, INRIA (National Institute for Research in Digital Science and Technology) – Biovision Lab,
    2. Professor Jean-Charles Régin, I3S – C&A (Constraints and Application) Lab
    3. Reseacher Aurélie Calabrèse, INRIA (National Institute for Research in Digital Science and Technology) – Biovision Lab,

    International partners : Professor Gordon E. Legge, Minnesota Laboratory for Low-Vision Research.

    Presentation of the PhD topic : 

    Reading performance evaluation has become one of the essential clinical measures for judging the effectiveness of treatments, surgical procedures, or rehabilitation techniques for low-vision patients. To create accurate reading tests, one needs to design a series of equivalent sentences in terms of linguistics, length and layout.

    However, because of their highly constrained nature, these sentences are hard to produce, leading to a very limited number of test versions. Your main mission will be to develop fundamental AI methods for automated natural language production, respecting strict constraints. Your results will have a direct impact on the design of powerful e-health diagnostic and rehabilitation tools that will benefit low-vision patients and their medical care staff. This project requires skills in computer science (algorithms, data structure, combinatorial optimization) and you should have a keen interest in medical applications.

    You will be part of a pluridisciplinary academic consortium, with three supervisors bringing their complementary expertise, and one international expert in low-vision for your 6-month secondment (Gordon E. Legge; University of Minnesota). You will also benefit from the participation of a private partner interested in natural language production (MantuLab Amaris Research Unit; Sophia Antipolis). Overall, this project will foster promising perspectives for your career, notably in the fields of natural language processing and AI, which are highly demanded in academia and industry.

    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. 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. 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. 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 : The PhD candidate will spend 6 months within the RECOD Lab at the Institute of Computing (IC) of the State University of Campinas (UNICAMP), in order to strengthen his international research carrier.

    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. 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. Researcher Mohamed MEHIRI, ICN (Institute of Chemistry of Nice, UMR CNRS 7272),
    2. Researcher Laurent BOYER,  C3M (Mediterranean Center for Molecular Medicine, INSERM U1065),

    International partners : Professor Giovanna Cristina VARESE, MUT (MYCOTHECA UNIVERSITATIS TAURINENSIS), University of Turin.

    Presentation of the PhD topic :

    Health problems and the quality of life are worldwide issues. The impact of antibiotic resistance on public health is considerable as it is estimated to be the leading cause of global mortality by 2050, resulting in more than 10 million deaths per year. Paradoxically, the pipeline for new antibiotics has experienced a long-term decline since 1987. The renewal of the therapeutic arsenal is therefore crucial in order to limit the impact of antibiotic resistance in the coming years.

    Marine microorganisms represent an under-explored source of new natural products which exhibit in situ several biological activities (cytotoxic, antibiotic, antifungal, antifouling, etc.). Marine natural products have often original structures, different from those of the metabolites of the terrestrial environment, and exhibit potent pharmacological activities with novel mechanisms of action. They could therefore be used to address unmet medical needs such as antibiotic resistance.

    In this context, the purpose of the e-MDR PhD project is the development of new antibiotics against clinical multidrug resistant bacteria from untapped marine microorganisms.

    The selected PhD candidate will conduct three concomitant tasks:

    Task1: Marine microorganisms cultivation (6-months secondment)

    Task2: Extraction, isolation and structural elucidation of new marine microbial natural products

    Task3: In vitro and In vivo antibacterial activities

    The PhD project will be developed thanks to the interdisciplinary combination of analytical/organic chemistry, and biochemistry studies. The project and the PhD candidate will benefit of the interdisciplinary activities of the two supervisors (Dr. M. MEHIRI & Dr. L. BOYER) and also of the expertise of the international collaborator (Pr. G. Cristina VARESE) for marine microoganisms strains cultivation (6-months secondment). This is a highly challenging and very promising approach that would pave the road for the discovery of new antibiotics and will therefore guarantee employability in universities and R&D companies.

    The PhD candidate should have strong backgrounds in analytical (chromatography, 1D and 2D NMR, HRMS, IR, UV, CD…) and organic chemistry. Knowledges and experiences in natural products chemistry and in biochemistry (biological screening of molecules) would be ideal.

    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 Jean-Yves DUBOZ, CRHEA-CNRS (Centre de Recherche sur l’Hétéro-Epitaxie et ses Applications)
    2. Researcher Joël Herault, Institut Méditerranéen de Proton Thérapie Centre Antoine Lacassagne

    International partners : Professor Andreas D. Wieck, University of Bochum (Germany)

    Presentation of the PhD topic :

    Proton therapy is currently the most advanced radiation therapy available for cancer treatment. Due to its specificities, proton beam can destroy cancer cells without attacking the surrounding healthy tissue. However, the proton beam position and shape must be accurately measured before each radiation since it directly affects the treatment efficiency and the eventual collateral damages. We propose a new calibration approach by developing robust GaN semiconductor detectors alloying to increase the control of the irradiated dose while strongly reducing the system complexity and cost. This innovation may thus drastically improve the proton therapy.

    In this context, the student will participate at all steps required to elaborate the GaN detectors at CRHEA-CNRS ( He/she will benefit of the access to the regional technological platform CRHEATEC in order to develop the different processes of these devices fabrication. In a second period, he/she will characterize the devices directly on the medical site using the proton beam of the IMPT-CAL (Institut Méditerranéen de Proton Thérapie – Centre Antoine Lacassagne, Subsequently, the student will spend 6 months in Professor Wieck's group at the University of Bochum to manufacture complete arrays of detectors but also to develop their interfacing with a commercially available readout circuit based on silicon.

    This thesis project offers a unique opportunity of contributing to the complete development of a novel electronic device dedicated to proton therapy, currently a very hot field of cancer treatment.

    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. Researcher Frédéric Luton, Institut de Pharmacologie Moléculaire et Cellulaire (IPMC),
    2. Researcher Olivier Pantz, Laboratoire de Mathématiques J. A. Dieudonné,

    International partners : Professor Keith E. Mostov, University of California, San Francisco.

    Presentation of the PhD topic :

    The proposed PhD project is transdisciplinary between computational modeling and cancer biology. Despite a considerable body of research, there is still no predictive marker to help clinicians discriminate between tumors that will remain benign from those that will develop into deadly invasive metastases.

    We wish to address this major public health issue by combining computational modeling with the study of cell culture models of invasion in vitro. The ultimate goal is to draw out a predictive tool for invasive cancers. The primary objective of the PhD project is to complete the mathematical modeling directing in silico simulations of the mechanical forces required for tumor invasion. Key parameters used to feed and refine the computational model will be obtained using a 3D-cell culture system and confocal microscopy, in order to identify cellular forces controlling invasion.

    The field of computational modeling, commonly used by all the big pharma but also other industries (aeronautic, automobile, civil engineering, etc), is perceived as an essential element in R&D and offers a wealth of career opportunities.

    The candidate will have a master in computational science, applied mathematics, bioinformatics or mechanic. The candidate should be familiar with modeling with partial differential equations, finite element methods, numerical analysis and programming in particular in Finite Element software (like FreeFem++). Some knowledge in cell biology will be appreciated.



    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 hamper 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 and machine learning, enabling an accurate quantification of gait deviation parameters during functional tests, including spatiotemporal and full-body kinematic parameters. For that purpose, the recruited Ph.D. student will design novel parametric continuous models providing accurate skeleton based gait representations, 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 computer animation, geometric modeling and machine learning adapted to our context, he or she will devise new fitting algorithms adapted to these models, in order to identify the best instance for a wide range of data sets. He or she will also participate to the acquisition of medical data (3D CGA) which are required 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, applied mathematics and computer science. He or she 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, Applied Mathematics or Biomechanics (motion analysis) and strong background in programming.

    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.


    1. Professor Christophe Den Auwer, ICN (Institute of Chemistry of Nice),
    2. Assistant Professor Sandra Perez, ESPACE laboratory,
    3. Col. Pharmacist Denis Josse, SDIS (Fire and Rescue Department) des Alpes-Maritimes,

    International partners: Researchers Johannes Raff, Satoru Tsushima, Helmoltz Zentrum Dresden Rossendorf (Germany); Professor Michael Kumke, University of Potsdam.

    Presentation of the PhD topic :

    Whether nuclear energy is being used as a source of energy or for other applications, it is subject to controversy: it tends to feed fears and diverse conspiracy theories at diverse scales. Behind those fears, risks in human contamination in case of an unprevented event are being questioned and the toxicology of plutonium in particular is a scientific challenge and a social stumbling block. The objective of this PhD thesis is double: better understand and model a specific Pu-protein interaction involved in human nuclear toxicology; question how scientific knowledge impacts public opinion on nuclear safety and as a result, how this modulates risks and crises management. On the biochemistry side, a model protein (phosvitin) will be selected and basics of plutonium-protein interaction mechanisms will be explored. On the sociology side, a parallel will be drawn between fundamental research in this field and public perception through the elaboration of a public survey on nuclear toxicology.

    The doctoral candidate will work with phosvitin protein that is an interesting phosphorylated model to understand interactions with plutonium. This will involve understanding its role in cell metabolism in collaboration with Dresden Rossendorf Institute and assessing its bio-inorganic chemistry using several spectroscopic techniques in collaboration with Univ. Potsdam and European synchrotron in Grenoble. The very original part of the project is that the fellow will also be involved in the elaboration of a public survey on nuclear toxicology because he will become the "scientific expert" of this field. This survey will be diffused to all Université Côte d'Azur students.

    The PhD candidate will be localized mainly at Université Côte d'Azur in Nice at the Institute of Chemistry of Nice in close collaboration with ESPACE laboratory and Rescuers from Alpes Maritimes. Secondments (a total of 6 months) are also foreseen at Rossendorf Institute in Dresden with J. Raff for biochemistry and at Univ. Potsdam for laser spectroscopy. Spectroscopic experiments will also be carried at the European synchrotron in Grenoble.

    The doctoral candidate should have a master in molecular inorganic chemistry or bio inorganic chemistry. A training in radiochemistry would be advantageous but is not compulsory.

    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 Philippe Le Thuc, LEAT (Laboratory of Electronics, Antennas and Telecommunications),
    2. Researcher Georges F. Carle, TIRO-MATOs, CEA (Alternative Energies and Atomic Energy Commission),

    International partners : Assistant Professor Hardik J. Pandya, Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore.

    Presentation of the PhD topic :

    While pre-clinical research remains today a pre-requisite in the validation of new drugs and therapies both for pharmaceutical companies as well as academic research, the need for reducing animal use (3R rules) requires the development of novel tracking systems able to collect biological data throughout their whole life. In order to match this challenge, we developed a communicating implantable tag combined with a set of antenna capable of recording the behaviour of hundreds of mice in their standard housing environment.

    The PhD candidate will participate in the upgrading of this pre-clinical eHealth system with the final goal of recording in real time biological and biochemical parameters H24, 7 days a week. To achieve this objective, several challenges will be addressed :
    - the miniaturization of the implanted device ;
    - the development of algorithms for Big Data analysis and the exploitation of these data by deep learning or AI (Artificial Intelligence) for better monitoring and health status prediction ;
    - the addition of sensors capabilities (temperature, pressure, enzymatic activities,…) to the identifying function of the tag.

    This project benefits from the expertise of the “Laboratoire d’Electronique, Antennes et Télécommunications“ (LEAT) for the development of the instrumented cages and of the implanted tags, and from the TIRO-MATOs laboratory expertise in mouse experimental models on aging and bone pathologies. The “Biomedical and Electronic Engineering Systems Laboratory“ (IIS, Bangalore), which will host the PhD student for six months, will provide through our collaboration the expertise for the development of advanced tags in biological parameters monitoring. The acquisition of such an expertise by the PhD candidate during her/his training, should provide numerous opportunities for a recruitment in pharmaceutical or telecommunication companies as well as in academic research. 


    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 Hervé Delingette, INRIA (French National Institute for computer science and applied mathematics),
    2. Medical Doctor Charles Raffaelli, CHU Nice (University Hospital of Nice).

    International partners : Assistant Professor Guillaume Lajoinie, Physics of fluids group, TechMed center for technical medicine and Mesa+ institute for nanotechnology, University of Twente (Netherlands).

    Presentation of the PhD topic :

    The prevalence of thyroid cancer is increasing worldwide, making it the fifth most common cancer among women. Owing to its low cost and high sensitivity, ultrasound imaging is unchallenged in the detection of thyroid nodules. The resulting diagnosis, however, heavily relies on the experience of the clinician and the interpretation is based on relatively subjective criteria.

    Given the worldwide shortage of expert sonographers and the increasing prevalence of thyroid cancer, there is a strong need to assist clinicians in their analysis of ultrasound images. The proposed thesis aims at developing software solutions based on Artificial Intelligence and more specifically deep learning neural networks, in order to help sonographers i) to select the most relevant planes of acquisition, ii) to objectively detect thyroid nodules and iii) to classify the nodules based on their malignancy. The originality of the proposed research project compared to the state of the art is twofold. First, it will rely on a close collaboration with the University Hospital of Nice, providing clinical expertise, curation of an extensive imaging database and access to state-of-the-art ultrasound devices with support from various industrial partners. Second, it will exploit potentially three-dimensional ultrasound data but also the time series of the raw radio-frequency signals acquired by the ultrasound probes, which potentially contains more information about the tissues than the classical image modality (B-mode). This work involving the physics of imaging will be performed in collaboration with University of Twente in the Netherlands, within a doctoral stay of 6 months in the laboratory “Physics Of Fluids”.

    The main activities of the PhD candidate will include i) the participation in the creation of an imaging database (imaging protocol, quality control and annotation tools), ii) the design of deep learning solutions for the selection of standard planes of acquisition, the detection and characterization of thyroid nodules and iii) the development of learning strategies to exploit RF signals for the characterization of nodules.



    How to apply?

    Application Procedure

    Deadline to submit: 20/03/2020 at 05:00 PM UTC+1 (CET)

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    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.

    Due to an important number of applications, the platform is experiencing some difficulties. To avoid any difficulties, please compress your file (7  MO maximum.  If you are still experiencing difficulties in submitting your application online, please send us your application as well as a screen shot of the filled online form at the following address:

    Apply now

    Deadline to submit: 20/03/2020 at 05:00 PM UTC+1 (CET)

    Click here to apply!

    Due to an important number of applications, the platform is experiencing some difficulties. To avoid any difficulties, please compress your file (7  MO maximum).  If you are still experiencing difficulties in submitting your application online, please send us your application as well as a screen shot of the filled online form at the following address: