DESCRIPTION :
With the help of the supervisor, the recruited person will develop computationally efficient methods to perform predictive simulations of human neuromechanical control (Julia language is preferred). They will propose extensions and improvements of the mathematical framework. Current tested methods involve stochastic optimal open-loop control approaches developed in [1-2] and tested or extended in [3-5]. They will eventually focus on integrating state-of-the-art muscle models in the simulations in collaboration with the partners. The candidate may also conduct experiments on human participants using various robotic interfaces and measurement techniques to test some predictions of the models, in close collaboration with other PhD/postdoc students from the lab.
For a better knowledge of the proposed research subject :
[1] Berret, B., & Jean, F. (2020). Efficient computation of optimal open-loop controls for stochastic systems. Automatica, 115, 108874.
[2] Leparoux, C., Bonalli, R., Hérissé, B., & Jean, F. (2024). Statistical linearization for robust motion planning. Systems & Control Letters, 189, 105825.
[3] Berret, B., Verdel, D., Burdet, E., & Jean, F. (2024). Co-contraction embodies uncertainty: An optimal feedforward strategy for robust motor control. PLOS Computational Biology, 20(11), e1012598.
[4] Berret, B., & Jean, F. (2020). Stochastic optimal open-loop control as a theory of force and impedance planning via muscle co-contraction. PLOS Computational Biology, 16(2), e1007414.
[5] Berret, B., Conessa, A., Schweighofer, N., & Burdet, E. (2021). Stochastic optimal feedforward-feedback control determines timing and variability of arm movements with or without vision. PLOS Computational Biology, 17(6), e1009047.
Collaboration :
Besides collaborations with the partners at University of Delaware, collaborations will be possible within the BOOST Inria team (Meriem Laleg), with ENSTA Paris (Frederic Jean), with Imperial College London (Etienne Burdet).
Travel :
* Potential travel is foreseen to meet colleagues at University of Delaware and present at conferences. Travel expenses are covered within the limits of the project.
Main activities:
* Develop efficient programs for predictive simulations of human neuromechanics
* Propose solutions for improving the mathematical framework
* Present the works' progress to partners
* Write and publish scientific papers
* Communicate results at conferences
Additional activities:
* Supervise students
Code d'emploi : Chargé de Recherches (h/f)
Domaine professionnel actuel : Scientifiques
Temps partiel / Temps plein : Plein temps
Type de contrat : Contrat à durée déterminée (CDD)
Compétences : Biologie Computationnelle, Programmation Informatique, Python (Langage de Programmation), MATLAB, Planification des Mouvements, Julia, Technologies Informatiques, Anglais, Français, Sens de la Communication, Esprit d'Équipe, Motivation Personnelle, Curiosité, Superviser les Étudiants, Écoute Active, Contrôle Automatique, Science Fondamentale, Biomécanique, Expérimentation, Mathématiques, Optimisation Mathématique, Mesure et Métrologie, Contrôle Moteur, Neurosciences, Équations Différentielles Ordinaires (CALCUL Différentiel), Recherche Post-Doctorale, Analyses Prédictives, Processus Stochastique, Conception et Réalisation en Robotique, Simulations, Etudes et Statistiques, Compétences de Modélisation, Publication / Edition
Courriel :
webmaster@inria.fr
Téléphone :
0139635511
Type d'annonceur : Employeur direct