PhD position in Reinforcement Learning for Automatic Discovery of Dynamical Systems from Data

Ovidiu Radulescu via dmanet <[email protected]>
Newsgroups gmane.science.mathematics.discrete
Message-ID <[email protected]>
We are offering a PhD position in Applied Mathematics and Machine 
Learning, focused on the intersection of reinforcement learning, 
optimization, and dynamical systems identification.

The project aims to develop reinforcement learning methods for the 
automatic discovery of nonlinear dynamical systems from data, combining 
ideas from:

     reinforcement learning and sequential decision-making

     system identification and inverse problems

     optimization and control theory

     representation learning for structured dynamical models

The goal is to design algorithms that can learn governing equations or 
compact dynamical representations from observations, with a focus on 
scalability, robustness, and theoretical guarantees. Applications will 
be used as motivating benchmarks, but the core emphasis is 
methodological: building general-purpose frameworks for data-driven 
discovery of dynamical systems.

This position is suited for candidates interested in theoretical and 
algorithmic aspects of modern ML, particularly those working at the 
interface of reinforcement learning, optimization, and mathematical 
modeling.

Location: Montpellier, France
Deadline: May 4

Applications must be submitted via the doctoral school ED I2S portal 
(Université de Montpellier):

https://edi2s.umontpellier.fr
→ PhD offers
→ Statistics and Data Science
→ LPHI – Reinforcement learning for automatic model discovery


Kind regards,

Ovidiu Radulescu


-- 
Prof. Ovidiu Radulescu
University of Montpellier
Laboratory of Pathogens and Host Immunity
Leader, Computational Systems Biology team
------------------------------------------------------------------------
LPHI - UMR 5294 CNRS/UM/INSERM   Phone: +33-(0)4-6714-9221
Pl. E. Bataillon - Bat. 24       Fax:   +33-(0)4-6714-4286
Université de Montpellier        email: [email protected]
CP 107
34095 Montpellier Cedex 5
FRANCE

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