BoostUrCAreer

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 launched 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, an 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 had to choose from. Now that the 15 ESRs have been selected, they will have 42 months to complete their PhD including the six-month mobility abroad (see the 15 PhD topics below).

 

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 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. 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. Benjamin Mauroy, laboratoire JA Dieudonné – VADER center, Nice
    2. Lisa Giovannini-Chami, Pneumology Department, Hôpital Lenval, Nice
    3. Angelos Mantzaflaris, INRIA Sophia Antipolis - Aromath

    International partners : Pr. Olivier Debeir, Laboratories of Image Synthesis and Analysis (LISA) of the Université Libre de Bruxelles

    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. 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. Doctor Marco Lorenzi, Epione Research Project, Inria Sophia Antipolis
      2. Doctor Barbara Bardoni, “RNA metabolism and neurodevelopmental disorders” Research Team, IPMC, CNRS

      International partners : Doctor Andre Altmann, Center of Medical Image Computing, University College London (UK)

      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

        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 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. 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. 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 (https://www.cogentech.it/index-en.php) 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 (https://www.ifom.eu/en/)


        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. 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.

         

         


        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.


        Doctoral Programme

        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.