Post-Doctoral Research Visit F/M Modeling the neuromechanics of human sensorimotor control

INRIA; Orsay; Sur place; EUR 40 000 - 60 000; Plein temps

Résumé du poste

Une institution de recherche en informatique recherche un postdoctorant en neuroscience computationnelle pour développer des simulations de contrôle neuromécanique humaine. Le candidat devra avoir des compétences solides en programmation et une bonne connaissance des méthodes d'optimisation. Le poste est basé à l'Université Paris-Saclay et se concentrera sur des projets de recherche innovants avec des partenaires internationaux. Ce rôle propose des avantages tels que des repas subventionnés, un remboursement partiel des frais de transport et une flexibilité au travail.

Prestations

Repas subventionnés

Remboursement partiel des frais de transport

7 semaines de congés annuels

Possibilité de télétravail

Accès à la formation professionnelle

Couverture de sécurité sociale

Qualifications

• Solides compétences en programmation requises.
• Connaissances en optimisation et équations différentielles appréciées.
• Niveau d'anglais parlé et écrit requis.

Responsabilités

• Développer des simulations prédictives de la neuromécanique humaine.
• Proposer des améliorations du cadre mathématique.
• Présenter l'avancement des travaux aux partenaires.
• Écrire et publier des articles scientifiques.
• Communiquer les résultats lors de conférences.
• Superviser des étudiants.

Connaissances

Compétences en programmation

Optimisation non linéaire

Optimisation stochastique

Équations différentielles stochastiques

Anglais

Description du poste

Contexte et atouts du poste

The position is within the Collaborative Research in Computational Neuroscience (CRCNS) project co-funded by the ANR and the NSF, which brings together an interdisciplinary team across Université Paris-Saclay (France; PI: Bastien Berret) and University of Delaware (US; PIs: Fabrizio Sergi & Joshua Cashaback).

The project aims at better understanding the control of force, mechanical impedance (via co-contraction of antagonist muscles), and feedback responses (via delayed/noisy sensory information) using computational models.

The position is located at Université Paris-Saclay (France) in the group of Prof. Bastien Berret (within CIAMS laboratory and BOOST Inria team led by Taous Meriem Laleg). The lab generally focuses on human motor control with applications to sports and health and the postdoc’s group is especially interested in human-robot interaction.

We invite applications for a two-year postdoctoral position (1-year extension possible)

Mission confiée

Assignments :

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. . Efficient computation of optimal open-loop controls for stochastic systems. Automatica, 115, 108874.

[2] Leparoux, C., Bonalli, R., Hérissé, B., & Jean, F. . Statistical linearization for robust motion planning. Systems & Control Letters, 189, 105825.

[3] Berret, B., Verdel, D., Burdet, E., & Jean, F. . Co-contraction embodies uncertainty: An optimal feedforward strategy for robust motor control. PLOS Computational Biology, 20, e1012598.

[4] Berret, B., & Jean, F. . Stochastic optimal open-loop control as a theory of force and impedance planning via muscle co-contraction. PLOS Computational Biology, 16, e1007414.

[5] Berret, B., Conessa, A., Schweighofer, N., & Burdet, E. . Stochastic optimal feedforward-feedback control determines timing and variability of arm movements with or without vision. PLOS Computational Biology, 17, 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.

Principales activités

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

Compétences

Technical skills and level required : Strong programming skills are needed for this project. Prior knowledge related to nonlinear optimization, stochastic optimization, stochastic / ordinary differential equations will be appreciated.

Languages : English

Relational skills : Team worker (verbal communication, active listening, proactivity, motivation and commitment).

Other valued appreciated : Good level of spoken and written English

Avantages

• Subsidized meals
• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Social security coverage

Rémunération

€2,788 gross per month