Université de Lille
3 weeks ago
3-Year Postdoc in Lille (France) for ML-Driven Phase Transitions in Nanoparticles University of Lille in France
Degree Level
Postdoc
Field of study
Chemistry
Funding
Available
Deadline
Sep 30, 2026
Country
France
University
Université de Lille
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About this position
Postdoctoral position at the University of Lille (UMET), France in machine-learning-enabled simulations of phase transitions in nanoparticles.
This 3-year, full-time postdoc is based in Lille in the UMET research environment and starts on 1 January 2027. The project investigates temperature-driven structural transformations in nanoparticles, with a focus on metallic alloys and oxides. The scientific goal is to dynamically track freezing and melting at the nanoscale, where finite-size and surface effects make phase behaviour especially rich and challenging.
The successful candidate will develop and benchmark machine-learning interaction potentials of increasing complexity, then use atomistic simulation approaches combining brute-force sampling and rare-event methods to identify and characterize nucleation pathways. The numerical results will be compared against state-of-the-art experiments carried out by collaborators in the same consortium.
This postdoc is particularly suited to researchers with backgrounds in computational chemistry, statistical physics, physical chemistry, thermodynamics, chemical physics, or computational physics. A PhD or equivalent is required. The announcement is aimed at recognised or established researchers (R2/R3). The team highlights close PI mentorship, a collaborative environment, and opportunities to supervise PhD and Master’s students.
Funding is not described as an EU Framework Programme grant, but the advert states that the post includes a competitive salary and French employment benefits such as healthcare, paid leave, and social protection.
Application deadline: 30 September 2026. To apply, send a CV, two references, and a cover letter explaining your motivation and suitability for the role to [email protected].
Funding details
Available
What's required
Applicants must hold a PhD or equivalent in computational chemistry or statistical physics. The position is aimed at recognised or established researchers (R2/R3). Experience in atomistic simulations, machine-learning interatomic potentials, phase-transition studies, and sampling methods would be highly relevant, although no formal language test or GPA requirement is stated.
How to apply
Send a CV, two references, and a cover letter explaining your interest in the position and what you can contribute. Apply by email to [email protected].
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