19 Postdoctoral Fellowship at Inria, France

This postdoctoral position is part of the national PEPR (Programme et Equipement Prioritaire de Recherche) PlantAgroEco project, coordinated by Alexis Joly. The PEPR involves several teams from various institutes (Inria ZENITH, CIRAD AMAP,  CIRAD PHIM, CIRAD PBVMT, INRAE ePhytia, INRAE IGEPP, INRAE LISAH, IRD EGCE, IRD IEES, Univ. Paris Saclay, TelaBotanica). The position is funded for 18 months, and will be conducted a Inria Lille – Nord Europe under the supervision of Odalric-Ambrym Maillard. This is a postdoctoral position in Machine Learning, more specifically in Reinforcement Learning.

Le centre de recherche Inria Saclay – Île-de-France, créé en 2008, accueille 450 scientifiques et 60 membres des services d’appui à la recherche. Les scientifiques sont organisés en 28 équipes de recherche dont 23 sont communes avec des partenaires du plateau de Saclay.

Le centre Inria Saclay – Île-de-France est un acteur essentiel de la recherche en sciences du numérique sur le plateau de Saclay. Il porte les valeurs et les projets qui font l’originalité d’Inria dans le paysage de la recherche : l’excellence scientifique, le transfert technologique, les partenariats pluridisciplinaires avec des établissements aux compétences complémentaires aux nôtres, afin de maximiser l’impact scientifique, économique et sociétal d’Inria.

This Post-doc subject is proposed in the context of the ROAD-AI Challenge involving the Cerema and the Inria Team ACENTAURI.

ACENTAURI is a robotic team located in Sophia Antipolis that studies and develop intelligent, autonomous and mobile robots that collaborate between them to achieve challenging tasks in dynamic environments. The team tackle perception, decision and control problems for multi-robot collaboration by proposing an original hybrid model-driven/data driven approach to artificial intelligence and by studying efficient algorithms. The team focus on robotic applications like environment monitoring and transportation of people and goods. In these applications, several robots will share multi-sensor information eventually coming from infrastructure. The effectiveness of the proposed approaches are demonstrated on real robotic systems like cars AGVs and UAVs together with industrial partners.

Methods for sampling and optimisation are often two sides of the same coin. A prominent example of this is statistical inference: sampling approaches typically aim at generating samples from the probability measure describing the unknown parameter given the data and prior knowledge, known as the Bayesian posterior distribution, while optimization approaches aim at calculating the maximum likelihood or maximum a posteriori estimator, i.e. the pointwise maximizer of the Bayesian posterior. Sampling and optimisation methods also coexist in the field of machine learning, where use of the former is driven by the need for explainable and transparent algorithms, which requires to analyse the parameter uncertainties present in trained neural networks. Many key recent developments in the fields of sampling and optimisation are based on the use of interacting particle systems and their analysis at the level of the nonlocal Fokker–Planck equation describing the systems in the limit of infinitely many particles, known as the mean field limit. This approach to numerical algorithms based on interacting particle systems emerged initially from the optimization community and has since then has brought considerable insight. It has enabled, notably, significant progress towards proving rigorously the longtime convergence of widely-used interacting particle methods, including the ensemble Kalman filter and particle swarm optimization.

The postdoctoral fellow recruited will join the Autonomous Pack collaborative project in partnership with GoodFloow, IRCICA, ITM Nord-Europe and ITM Atlantique. The Autonomous Pack project aims to reduce CO2 emissions linked to packaging waste from industries within supply chains. The aim is to study the use of IoT devices attached to reusable packaging, which is generally more expensive. Due to the unprofitability of packaging linked to the lack of responsibility when it is altered, the switch from disposable to reusable packaging by manufacturers remains difficult and is being slowed down. In order to speed up this migration, the solution studied in Autonomous Pack consists of developing multi-technology wireless communication IoT devices that embed Artificial Intelligence (AI) for real-time identification of the various stages in the supply chain. Depending on these stages, predefined actions are executed according to a state machine to transparently assign responsibility for the reusable pack to a person or entity.

This postdoctoral position is part of the national PEPR (Programme et Equipement Prioritaire de Recherche) 5G NF-FITNESS project, coordinated by Daniel Koman, IMT. The PEPR involves several teams from various institutes (CEA, IMT, INRIA, Eurecom, CNRS, Centrale Supelec, INP Toulouse, Nantes Université, ESIEE Paris-UGE). The position is funded for 18 months, and will be conducted at Inria Lille – Nord Europe under the supervision of Nathalie Mitton (Inria) and Patrick Sondi (IMT Nord Europe). This is a postdoctoral position in Vehicle-to-Everything (V2X) communications, more specifically in security and audit of V2X communications.

This is a collaborative research project involving 2 partners.

Jing-Rebecca Li, Researcher (Applied Mathematics); Inria-Saclay, Équipe Idefix (Medical Imaging, Applied Mathematics); ENSTA Paris, Unité de Mathématiques Appliquées (UMA).  Institut Polytechnique de Paris.

The present postdoctoral project is part of a collaborative project between the Atlantis project-team from the Inria Research Center at Université Côte d’Azur,  (2) Thales R&T in Palaiseau, France, which is the main multidisciplinary research center of Thales group, and one of the main world players in professional electronics (3) and Nanoe in Ballainvilliers, France, whcih is  a SME (Small and Medium Entreprise) specialized  in development and fabrication of innovative ceramic materials for the industries.

Atlantis is  a joint project-team  between Inria and  the Jean-Alexandre Dieudonné Mathematics Laboratory at  Université Côte d’Azur. The team  gathers applied mathematicians and  computational scientists who are collaboratively undertaking  research activities aiming at the design, analysis, development and  application of innovative numerical methods for systems of  partial differential equations (PDEs) modelling nanoscale light-matter interaction problems. In this context, the team is  developing  the   DIOGENeS  [https://diogenes.inria.fr/]  software suite,  which  implements  several Discontinuous  Galerkin  (DG)  type methods tailored to the systems  of time- and frequency-domain Maxwell equations  possibly coupled  to  differential  equations modeling  the behaviour of propagation  media at optical frequencies.  DIOGENeS is a unique  numerical   framework  leveraging   the  capabilities   of  DG techniques  for  the simulation  of  multiscale  problems relevant  to nanophotonics and nanoplasmonics.

We’re looking for someone with experience in neuroimaging of social cognition who can contribute meaningfully to this new era of hyperscanning research. Particularly, we are seeking someone who can assist in the design of rigorous naturalistic hyperscanning paradigms which investigate social phenomena such as collaboration, rapport, learning, and performance, and the analysis of the resulting data, preferably using newer machine learning analytic tools. In addition to investigating understudied phenomena of social cognition, these findings will eventually be utilized to improve a virtual peer – a conversational agent that builds bonds with children in the service of learning.

Ce sujet concerne la sécurité des données personnelles dans le cadre d’applications citoyens cherchant à analyser de grands volumes de données personnelles. Spécifiquement, le projet est focalisé sur le big data décentralisé et sécurisé, avec comme objectif, le calcul sur des données personnelles fournies par un très grand nombre d’individus, pour permettre, par exemple, la classification automatique d’écritures bancaires.

L’équipe Petrus travaille sur la problématique du cloud personnel sécurisé à base de matériel sécurisé côté utilisateur, à savoir, le développement d’une plateforme permettant à tout individu de rapatrier, stocker, gérer, utiliser et partager d’une manière sécurisée l’ensemble de ses données personnelles.

The Inria centre at Université Côte d’Azur includes 37 research teams and 8 support services. The centre’s staff (about 500 people) is made up of scientists of different nationalities, engineers, technicians and administrative staff. The teams are mainly located on the university campuses of Sophia Antipolis and Nice as well as Montpellier, in close collaboration with research and higher education laboratories and establishments (Université Côte d’Azur, CNRS, INRAE, INSERM …), but also with the regiona economic players.

With a presence in the fields of computational neuroscience and biology, data science and modeling, software engineering and certification, as well as collaborative robotics, the Inria Centre at Université Côte d’Azur  is a major player in terms of scientific excellence through its results and collaborations at both European and international levels.

(18) Postdoctoral Fellowship Position

Postdoc Fellowship Position summary/title: Post-Doctoral Research Visit F/M Predicting communication networks in protein-nucleic acid complexes

Deadline : 2023-12-20

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This postdoctoral position is part of the national PEPR (Programme et Equipement Prioritaire de Recherche) PlantAgroEco project, coordinated by Alexis Joly. The PEPR involves several teams from various institutes (Inria ZENITH, CIRAD AMAP,  CIRAD PHIM, CIRAD PBVMT, INRAE ePhytia, INRAE IGEPP, INRAE LISAH, IRD EGCE, IRD IEES, Univ. Paris Saclay, TelaBotanica).  This is a postdoctoral position in Machine Learning, more specifically in Reinforcement Learning. We are seeking a highly motivated and skilled postdoctoral fellow to join the project, dedicated to advancing the field of Machine Learning, with a specific focus on Reinforcement Learning. The position is initially funded for 18-month, but it can be easily extended.