Post-Doctoral Research Visit F/M Neural Gain & Adaptive Learning (LENGA Project)

Inria, the French national research institute for the digital sciences; France; Sur place; EUR 40 000 - 60 000; Plein temps

Résumé du poste

A leading research institute in France seeks a full-time Mathematics Researcher to join a project on brain networks, focusing on innovative neurotechnologies for research and clinical applications. Candidates must have a strong background in recurrent neural networks and proficiency in Python. The role involves modeling adaptive learning mechanisms, analyzing experimental data, and contributing to published research. The position offers a gross salary of 2788 € per month, along with generous leave and teleworking options.

Prestations

Partial reimbursement of public transport costs

7 weeks of annual leave

Possibility of teleworking

Access to vocational training

Qualifications

• Strong background in recurrent neural networks (rate-based & spiking).
• Proficiency in Python, especially scientific libraries and simulation frameworks.
• Experience analysing behavioral or electrophysiological data is a plus.

Responsabilités

• Contribute to modeling and analysis of adaptive learning mechanisms.
• Evaluate performance across behavioral and computational contexts.
• Formulate testable predictions for experimental validation.

Connaissances

Recurrent neural networks expertise

Proficiency in Python

Experience with scientific libraries (NumPy, SciPy)

Understanding of dynamical systems

Data analysis skills

Formation

Advanced degree in a relevant field

Outils

Brian2

NEST

Description du poste

Inria, the French national research institute for the digital sciences

Organisation/Company Inria, the French national research institute for the digital sciences Research Field Mathematics Researcher Profile Recognised Researcher (R2) Country France Application Deadline 18 Dec 2025 - 00:00 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 38.5 Offer Starting Date 1 Apr 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number 2025-09545 Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Cophy is a project team between Inria, Inserm and CRNS, which gathers an international team of researchers, engineers, clinicians and students interested in studying brain networks, to shed light on information processing, its modulation by attention, prediction and learning, as well as the intricate coupling between action and perception. Our research combines (1) cross-species in-vivo observations of brain electrical and neurotransmitter dynamics in health and pathology; (2) in silico models, including Bayesian models, neural mass models and spiking neural networks; (3) in vitro neuronal network measurements. Our aim is to innovate in neurotechnologies in the broadest sense, both for research and for clinical applications, particularly in neurodevelopmental disorders.

Adaptive behavior depends on selecting advantageous actions while avoiding detrimental ones, a process that requires continuously updating the relationship between actions and outcomes based on experience. In stable environments, such adaptation can rely on gradual adjustments in learning rates, but in dynamic contexts, flexibility demands faster mechanisms that preserve prior knowledge while enabling rapid behavioral change. This raises a fundamental question: how does the brain achieve immediate adaptation without relying solely on slow synaptic modification?

Our recent theoretical and experimantal work explores how dynamic mechanisms operating at the network level may enable rapid behavioral adaptation alongside more traditional forms of learning. This framework seeks to bridge fast, state-dependent computations and slower, experience-driven plasticity, contributing to a more unified understanding of behavioral adaptation.

The project aims to:

• Develop and analyze computational models that capture flexible, multi-timescale learning and adaptation in recurrent neural circuits.
• Test model predictions in behavioral experiments.
• Investigate how principles of biological adaptability can inform the design of efficient and robust learning algorithms for artificial systems.

The candidate will contribute to modeling and analysis of adaptive learning mechanisms, evaluation of their performance across behavioral and computational contexts, and formulation of testable predictions for experimental validation. The recruited person will be in connection with Romain Ligneul and Renato Marciano Maciel from the Cophy Team.

References:

• E. Behrens, M. W. Woolrich, M. E. Walton, and M. F. Rushworth, “Learning the value of information in an uncertain world,” Nature Neuroscience, vol. 10, no. 9, pp. 1214–1221, 2007.
• A. Ferguson and J. A. Cardin, “Mechanisms underlying gain modulation in the cortex,” Nature Reviews Neuroscience, vol. 21, no. 2, pp. 80–92, 2020.
• D. Grossman and J. Y. Cohen, “Neuromodulation and neurophysiology on the timescale of learning and decision-making,” Annual Review of Neuroscience, vol. 45, pp. 317–337, 2022.
• Kim, Y. Li, and T. J. Sejnowski, “Simple framework for constructing functional spiking recurrent neural networks,” PNAS, vol. 116, pp. 22811–22820, 2019.
• Köksal-Ersöz, P. Chossat, and F. Lavigne, “Gain modulation of actions selection without synaptic relearning,” PLoS ONE, 20(9): e0333350, 2025.
• Mei, E. Muller, and S. Ramaswamy, “Informing deep neural networks by multiscale principles of neuromodulatory systems,” Trends in Neurosciences, vol. 45, pp. 237–250, 2022.
• Ligneul and Z. F. Mainen, “Serotonin,” Current Biology, vol. 33, pp. R1216–R1221, 2023.
• Design, implement and optimise learning rules
• Process electrophysiological and behavioural datasets.
• Run numerical simulations to explore different learning time‑scales and environmental conditions.
• Work closely with the experimental team.
• Writing research papers for submission to top-tier conferences and journals in the field
• Disseminating research findings through presentations at conferences, seminars, and workshops.
• Strong background in recurrent neural networks (rate‑based & spiking).
• Prior work on learning algorithms.
• Familiarity with neuromodulatory concepts
• Familarity with dynamical systems
• Experience analysing behavioural or electrophysiological data is a plus.
• Proficiency in Python, especially scientific libraries (NumPy, SciPy) and simulation frameworks (Brian2, NEST).
• Ability to work autonomously and in interdisciplinary teams.
• Good scientific writing (English) and presentation skills.

Specific Requirements

• Strong background in recurrent neural networks (rate‑based & spiking).
• Prior work on learning algorithms.
• Proficiency in Python, especially scientific libraries (NumPy, SciPy) and simulation frameworks (Brian2, NEST).

Languages FRENCH Level Basic

Languages ENGLISH Level Good

Additional Information

• 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

2788 € gross salary / month

Selection process

Defence security: This position is likely to be assigned to a restricted area (ZRR), as defined in decree no. 2011-1425 relating to the protection of the nation's scientific and technical potential (PPST). Authorisation to access a zone is issued by the head of the establishment, following a favourable ministerial opinion, as defined in the decree of 03 July 2012 relating to the PPST. An unfavourable ministerial opinion for a post assigned to a ZRR would result in the recruitment being cancelled.

Applications must be submitted online via the Inria website. Processing of applications submitted via other channels is not guaranteed.