Dans le cadre de son programme Initiative d’Excellence UCAJEDI et plus particulièrement de l'action 6.2 de ce programme consacrée au soutien aux nouvelles équipes et aux thématiques émergentes, la ComUE Université Côte d’Azur lance un appel à projet visant à soutenir le développement des compétences d'Université Côte d'Azur dans le domaine de l'intelligence artificielle et du traitement des données massives, via le recrutement de jeunes chercheurs et chercheuses au niveau post-doctoral. Les applications au domaine de la santé seront examinées avec un intérêt particulier, mais pas ne sont pas exclusives : les autres domaines d'application sont également recevables.
L’appel est doté pour permettre le financement de 4 contrats de 18 mois.
Calendrier :
Télécharger les documents :
Contact : Diana Sebbar
Résultats de l’AAP :
Compte tenu de l’excellence des propositions et du caractère stratégique de la thématique de l’AAP, Université Côte d’Azur, sur proposition du comité d’évaluation, a décidé de financer 3 contrats postdoctoraux en sus des 4 prévus par l’AAP, soit 7 au total :
Titre du projet |
Nom du porteur |
Laboratoire |
Graduate School |
Appel à candidatures |
Co-clustering of Massive Longitudinal Data with Applications to Sociology | Charles BOUVEYRON et Elena EROSHEVA | LJAD / INRIA | DS4H | Voir l'appel à candidaturesJob announcement for a 18-months postdoctoral position Co-clustering of Massive Longitudinal DataProjectLongitudinal data that are collected over time are ubiquitous in sociological, behavioral, and medical studies. Longitudinal data are different from functional data and from time series in multiple aspects in- cluding non-regular and infrequent observation intervals, measurement error susceptibility, and presence of missingness due to staggered study entry or dropout. Modern technologies such as smartphones, wearable bands, and smart watches, provide convenient options for collecting such longitudinal data on massive number of individuals as well as massive number of variables over time. In this setting, efficient summaries of information over both dimensions, individuals and variables, are of particular interest to researchers.
Work environmentThe candidate will have an office located in the Maasai, INRIA joint-team with Université Côte d’Azur at Sophia-Antipolis, Nice, France. INRIA and UCA campuses offer a vibrant and stimulating work environment. This project is aligned with the objective of the UCA Jedi program, in particular with the Data Science strategic program, and is a funding element of the new UCA/Inria joint team Maasai, which is headed by Charles Bouveyron and which was the result of the UCA Jedi initiative. This project will have strong ties and a possibility of a short visit to the department of Statistics of the University of Washington, Seattle, USA, through collaboration with Elena Erosheva, UCA International Chair in Data Science and Professor of Statistics and Social Work at the University of Washington, Seattle. This project is also part of the Institut 3IA Côte d’Azur that has been recently funded by the French AI initiative. How to applyPlease email you application to both Charles Bouveyron, charles.bouveyron@univ-cotedazur.fr, and Elena Erosheva, erosheva@uw.edu, by 1st October 2019, including the words “UCA-post-doc” in the e-mail subject line. Women, persons with disabilities, and underrepresented minorities are especially encouraged to apply.
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Modeling and Analysis of Complex and Massive Heterogeneous Data with Deep Generative Models | Frédéric PRECIOSO et Charles BOUVEYRON | I3S /LJAD | DS4H | Voir l'appel à candidaturesJob announcement for a 18-months postdoctoral position Modeling and Analysis of Complex and MassiveHeterogeneous Data with Deep Generative ModelsAugust 27, 2019 ProjectArtificial intelligence has become a key element in most scientific fields and is now part of everyone life thanks to the digital revolution. Statistical, machine and deep learning methods are involved in most scientific applications where a decision has to be made, such as medical diagnosis, autonomous vehicles or discourse analysis. Such methodologies have also significant implications in fields where the understanding of a phenomenon from data is needed. This is the case for instance in medicine, biology, astrophysics or digital humanities where learning methods allow to recover hidden patterns in the data and to visualize them. The recent and highly publicized results of artificial intelligence should not hide the remaining and new problems posed by modern data. Indeed, despite the recent improvements due to deep learning, the nature of modern data have brought specific issues. For instance, learning with high-dimensional, atypical (networks, functions, ...), dynamic, or heterogeneous data remains difficult, for theoretical and algorithmic reasons. The recent establishment of deep learning has also open new questions in this context such as: How to learn in an unsupervised or weakly-supervised context with deep architectures? How to learn with evolving and corrupted data? This project will focus on the development of learning models and algorithms that are able to handle complex heterogeneous data. We in particular target data which involved both structured elements (such as contextual fields, meta-data, ...) and non-structured ones (such as texts, images, ...). For instance, we target the automatic analysis of medical heterogeneous data that can consist in the all available patient data (biological data, MRI/TEP images, functional measures, omic data) and all contextual elements about the patient (clinical path, surgery reports, doctor letters). The analysis of such massive data may result in a significant improvement of both the medical diagnosis and the patient treatment process. Another application field that we profile as a possible application is the analysis of intellectual property data, such as patents or scientific publications. There is indeed a strong need for private companies and public institutes to be able to manage and evaluate the value of their intellectual property. Scientific publications and patents share to be made of both structured elements (contextual fields, meta-data, networks, ...) and non-structured ones (texts, images, schemata, ...). Although heterogeneous data are indeed parts of the most important and sensitive applications of artificial intelligence, there is a lack of available methods able to deal with such data. Learning methods usually are only able to handle one type of data (continuous data or texts, or images, ...), with eventually some covariates (contextual data). For instance, the most popular method to cluster documents is the latent Dirichlet allocation (LDA) model [3]. For image analysis, convolutional neural networks [4] are nowadays the most efficient algorithms to describe and classify natural images. Beyond single-type data models, proposing unified models for heterogeneous data is an ambitious task, but first attempts (e.g. the Linkage project [1, 2] for instance) on combination of two data types have shown that more general models are feasible. Those models turned out to significantly improve the performances. In this postdoctoral project, we will address the problem of conciliating structured and non-structured heterogeneous data, as well as data of different levels (individual and contextual data). We ambition in this research project to address two main learning problems: clustering the heterogenous data by taking into account all available information and predicting the value of a subset of key elements (which may be viewed as a regression problem). Interestingly, the two problems may be combined if, for the second situation, we do not have a preselected subset of elements to evaluate. The clustering method that will be proposed may be used to propose such a subset of elements. Candidate ProfileThe ideal candidate will have a strong academic backgrounds in computational, applied statistics and deep learning, and a desire to work on challenging problems in artificial intelligence. Experience in model-based clustering and deep generative models is beneficial but not a necessary condition.
Work environmentThe candidate will have an office located in the Maasai, INRIA joint-team with Université Côte d’Azur at Sophia-Antipolis, Nice, France. INRIA and UCA campuses offer a vibrant and stimulating work environment. This project is aligned with the objective of the UCA Jedi program, in particular with the Data Science strategic program, and is a funding element of the new UCA/Inria joint team Maasai, which is headed by Charles Bouveyron and which was the result of the UCA Jedi initiative. This project is also part of the Institut 3IA Côte d’Azur that has been recently funded by the French AI initiative. How to applyPlease email you application to both Charles Bouveyron, charles.bouveyron@univ-cotedazur.fr, and Frédéric Precioso, frederic.precioso@univ-cotedazur.fr, by 1st October 2019, including the words “UCA-post-doc” in the e-mail subject line. Women, persons with disabilities, and underrepresented minorities are especially encouraged to apply. The application should contain:
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Deep Neural Networks – Assisted Face Analysis for Health Monitoring | Francois BREMOND et Antitza DANTCHEVA | INRIA (STARS) / CobTek | DS4H / NGLS / HEALTHY | Voir l'appel à candidaturesDeep Neural Networks – Assisted Face Analysis for Health MonitoringResearch field: Computer Vision based Facial Analysis Project-team: STARS, Inria Sophia Antipolis About Inria and the teamInria, the French National Institute for computer science and applied mathematics, promotes “scientific excellence for technology transfer and society”. Graduates from the world’s top universities, Inria's 2,700 employees rise to the challenges of digital sciences. With its open, agile model, Inria is able to explore original approaches with its partners in industry and academia and provide an efficient response to the multidisciplinary and application challenges of the digital transformation. Inria is the source of many innovations that add value and create jobs. Team Team web site MissionThe Post Doc position is within the framework of the UCA Postdoctoral Fellowship DNN4HM.
Job descriptionThe Inria STARS team is seeking for a Post Doctoral researcher with strong background in computer vision, deep learning, machine learning and applied mathematics. The proposed research project Deep Neural Networks for Health Monitoring (DNN4HM) aims to provide computer vision methods for facial expression recognition in patients with Alzheimer's disease (AD). Most importantly, the work seeks to be a part of a paradigm shift in current healthcare in finding effective, cost-efficient and objective measures to assess different therapy treatments, as well as to enable automated human-computer interaction in remote large-scale healthcare frameworks. First objective is to propose a model for facial expression recognition in healthcare settings, where we aim to develop a general approach, where expression models are averaged over the set of subjects and a subject based approach, where we consider subjects individually and classifiers are trained using general expression analysis. Second objective is to fuse and integrate different methods with respect to different user needs and environmental requirements including hospital configuration and sensor setting, as well as ethical issues. Skills and profileCandidates must hold a Ph.D. in Computer Science or a closely related discipline. Candidates must also show evidence of research productivity (e.g. papers, patents, presentations, etc.) at the highest level. Advantages
Additional Information
ApplicationTo apply, please email the following documents to Antitza Dantcheva (antitza.dantcheva@inria.fr), indicating “UCA – Post Doc” in the e-mail subject line:
The submission deadline is November 2019. Nevertheless, the application may be closed before the limit date, if a satisfying candidate is found. Please do not hesitate to contact us for any inquiry. Inria's disabilities policy: All positions at the institute are open to disabled people. Security and defense procedure |
Extraction of curvilinear structure networks in image data using an innovative deep learning approach: application to fracture and fault network extraction from satellite data | MANIGHETTI Isabelle et ZERUBIA Josiane | Géoazur/ INRIA | INCISE | Voir l'appel à candidaturesExtraction of curvilinear structure networks in image data using an innovative deep learning approach: application to fracture and fault network extraction from satellite dataApplication before July 31, 2019 18 months post-doc position on project “Extraction of curvilinear structure networks in image data using an innovative deep learning approach: application to fracture and fault network extraction from satellite data” Project Curvilinear structure networks are widespread in both nature and anthropogenic systems, ranging from angiography, earth and environment sciences, to biology and anthropogenic activities. Recovering the existence and architecture of these curvilinear networks is an essential and fundamental task in all the related domains. At present, there has been an explosion of image data documenting these curvilinear structure networks. Therefore, it is of upmost importance to develop numerical approaches that may assist us efficiently to automatically extract curvilinear networks from image data. In recent years, a bulk of works have been proposed to extract curvilinear networks. However, automated and high-quality curvilinear network extraction is still a challenging task nowadays. This is mainly due to the network shape complexity, low-contrast in images, and high annotation cost for training data. To address the problems aroused by these difficulties, this project intends to develop a novel, minimally-supervised curvilinear network extraction method by combining deep neural networks with active learning, where the deep neural networks are employed to automatically learn hierarchical and data-driven features of curvilinear networks, and the active learning is exploited to achieve high-quality extraction using as few annotations as possible. Furthermore, composite and hierarchical heuristic rules will be designed to constrain the geometry of curvilinear structures and guide the curvilinear graph growing. The proposed approach will be tested and validated on extraction of tectonic fractures and faults from a dense collection of satellite and aerial data and “ground truth” available at the Géoazur laboratory in the framework of the Faults_R_Gems project co-funded by the University Côte d’Azur (UCA) and the French National Research Agency (ANR). Then we intend to apply the new automatic extraction approaches to other scenarios, as road extraction in remote sensing images of the Nice region, and blood vessel extraction in available medical image databases. Candidate profile Strong academic backgrounds in Stochastic Modeling, Deep Learning, Computer Vision, Remote Sensing and Parallel Programming. A decent knowledge of Earth and telluric features (especially faults) will be appreciated. At UCA, Géoazur and Inria we seek to increase the number of women in areas where they are under-represented and therefore we explicitly encourage women to apply. We are also committed to increasing the number of individuals with disabilities in our workforce and therefore we encourage applications from such qualified individuals. Post-doc salary and conditions Duration: 18 months Starting date: between September 1st and November 1st, 2019 Work conditions: Tight collaboration between Géoazur and Inria-SAM (http://www- sop.inria.fr). Position located at Géoazur, with research discussions planned twice a week at Inria Sophia. How to apply Dead-line to apply: July 31, 2019 Please email a full application to both Isabelle Manighetti (manighetti@geoazur.unice.fr) and Josiane Zerubia (josiane.zerubia@inria.fr), indicating “UCA-AI-post-doc” in the e-mail subject line. The application should contain: Contacts : Isabelle Manighetti : manighetti@geoazur.unice.fr |
Searching for pre-earthquake signal with AI | Jean-Paul AMPUERO et Quentin BLETERY | Géoazur | INCISE | |
Targeting ion transport in Cancer Metabolism and Invasion | Laurent COUNILLON | LP2M | NGLS | Voir l'appel à candidaturesTargeting ion transport in Cancer Metabolism and InvasionOur laboratory, LP2M UMR7370 is recruiting an international post doctoral fellow for a period of 18 month starting if possible as early as September 2019. The candidate will work on a transdisciplinary project aimed at deciphering the relations between the activities of Na+/H+ exchangers, Na+ Channels and Na/K ATPase in the field of cellular metabolic reprogrammation, motility and invasiveness. The experimental approaches will combine ion transport and cellular measurements with transcriptomics and metabolomics and will therefore involve the analysis of large sets of data. The candidate will benefit from the infrastructures of LP2M and of the surrounding technical platforms: Electrophysiology, atomic absorption spectrometry analytical HPLC, flow cytometry, videomicroscopy, genomics, imaging, metabolomics... The candidate will also work in collaboration with colleagues from the Maths/Physics and computing departments for data analysis and model construction. The candidate will have to possess a high-level expertise in ion transport, cellular biology and metabolism to perform the experimental parts of the project. An expertise in mathematical and or computational approaches of biology is not mandatory but will be appreciated. Additional information: Approximate net salary : 2400€/month Laboratory website: http://unice.fr/lp2m/fr Please send a detailed CV and two names of scientific colleagues or former supervisors, for reference to Pr. Laurent Counillon : Laurent.Counillon@univ-cotedazur.fr |
Single-cell multi-omics analysis of human airways | Pascal BARBRY | IPMC | NGLS | Voir l'appel à candidaturesAnalysis and biological interpretation of multi-omics data in the Human Lung AtlasResearch field: Single cell genomics, cell biology, personalized medicine, machine learning Team: Pascal Barbry’s laboratory, IPMC Sophia Antipolis (CNRS & Université Côte d’Azur ), France About IPMC and the teamFounded in 1989 in the Scientific Park of Sophia Antipolis (French Riviera), IPMC is a joint laboratory between the Centre National de la Recherche Scientifique (CNRS) and Université Côte d’Azur. It explores with industrial and academic partners original approaches and models at the front-end of research in biology, genomics, molecular and cellular pharmacology. Team Team web site MissionThe Post Doc position is within the framework of the UCA Postdoctoral Fellowship.
Job descriptionThe group of Pascal Barbry is hiring a Post-Doctoral researcher with strong background in bioinformatics, machine learning and applied mathematics. The candidate will work on the integration of data from several single-cell experiments and techniques already or currently deployed by the host laboratory, with the final goal of unraveling the sequences of molecular events controlling the balance between distinct airway cell types during health and disease. By deciphering the epithelial-related molecular mechanisms it is anticipated that more efficient therapies will be possibly set up. Our team contributes to the international Human Lung Atlas seed network, which works on the construction of the first comprehensive human lung cell atlas. The Post-Doctoral researcher will analyze the datasets produced in our laboratory and by collaborators in the framework of this consortium, including datasets currently produced by our laboratory and studying alterations of the airways in severe asthma (remodeled airway epithelium, hyperplasia of mucus-secreting cells, etc). The postdoc will analyze with dedicated computational approaches a huge database incorporating a full genome investigation on more than 100 000 cells. The main challenge of this multiscale project will be to establish genome-wide RNA expression, splicing and genome accessibility through short and long reads single cell sequencing. The resulting datasets will be also merged with spatial transcriptomics data. One idea will be to establish in a multidimensional gene expression space possible trajectories between a specific progenitor cell and different types of terminally differentiated cells. Exact spatial distribution of cell types in the biological tissue will then be connected to this reconstructed gene expression space. The post-doctoral candidate will work on four subtasks:
Skills and profile
Advantages
Additional Information
ApplicationTo apply, please email the following documents to Pascal Barbry (barbry@ipmc.cnrs.fr), indicating “UCA – Post Doc” in the e-mail subject line:
The submission deadline is November 2019. Nevertheless, the application may be closed before the limit date, if a satisfying candidate is found. Please do not hesitate to contact us for any inquiry. IPMC's disabilities policy: All positions at the institute are open to disabled people. Security and defense procedure: In the interests of protecting its scientific and technological assets, IPMC is a restricted-access establishment. Consequently, it follows special regulations for welcoming any person who wishes to work with the institute. The final acceptance of each candidate thus depends on applying this security and defense procedure. Recent publications from the laboratory:
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