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Portail > Offres > Offre UMR3571-CHRVES-001 - Fonctionnelles de la prise de décision et de la navigation chez les insectes H/F

Postdoctoral position on functionals of decision making and navigation in insects H/F

This offer is available in the following languages:
Français - Anglais

Date Limite Candidature : mardi 14 décembre 2021

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General information

Reference : UMR3571-CHRVES-001
Workplace : PARIS 15
Date of publication : Tuesday, November 23, 2021
Type of Contract : FTC Scientist
Contract Period : 24 months
Expected date of employment : 1 February 2022
Proportion of work : Full time
Remuneration : between 2744 and 4072 euros gross monthly depending on experience
Desired level of education : PhD
Experience required : Indifferent

Missions

The objective of this project is to model and understand how small animals, such as insects, can navigate in complex varying environments, learn the statistical properties of their environment to adapt decision making, and can integrate large amounts of sensory inputs and encode internal states (for example stress) that influence probabilistic responses to external signals.

We focus in the lab on decision making and evasive behavior in Drosophila larva and on navigation of insects and unicellular organisms.

Activities

The post-doctoral researcher will work on developing theoretical and data-driven computational models for navigation based on sparse learning of functionals of noisy or limited sensory signals.

Skills

Required skills:
- PhD in mathematics, theoretical physics, or similar.
- Knowledge of stochastic processes and statistical analysis.
- Programming experience, ideally in Python, Julia or C/C++.

Nice-to-have skills:
- Random walk theory.
- Experience in Reinforcement learning or bandit problems.

Work Context

The work will be performed in the Decision and Bayesian Computation (DBC) lab at Institut Pasteur in Paris. The lab is focused on the algorithms and computation selected by evolution to perform biological decision-making. We address this topic with an interdisciplinary approach mixing statistical physics, Bayesian machine learning, and information theory.
The lab works in close connection with experimental collaborators to design experiments and obtain experimental data on animal behavior, notably from Drosophila larvae and bacteria.

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