Memory decomposition for long range effect in atomistic simulations

Université de Caen Normandie; France; Sur place; EUR 30 000 - 50 000; Plein temps

Résumé du poste

A leading research institution in France is seeking a Postdoc in Chemistry with a focus on molecular simulations and computational methods. The ideal candidate will have a PhD and a strong background in mathematics or physics, along with expertise in numerical analysis and programming. This role offers an opportunity to address challenges in simulating high-dimensional systems and integrate memory effects into simulations. Located in Caen, this position involves a full-time temporary contract.

Qualifications

• Strong computational/mathematical background.
• Prior scientific programming experience.
• Enthusiasm for code development.

Responsabilités

• Numerical analysis and implementation of noise extraction.
• Developing and testing computational methods.
• Validating against atomistic simulation data.

Connaissances

Inference of stochastic dynamics

Numerical analysis

Mathematical analysis

Machine learning force fields

Data-driven techniques for chemistry or materials science

Formation

PhD in Mathematics, Physics, Chemistry or related field

Outils

Scientific programming

Description du poste

Organisation/Company Université de Caen Normandie Research Field Chemistry Researcher Profile Recognised Researcher (R2) Positions Postdoc Positions Country France Application Deadline 30 Nov 2025 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Jan 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Molecular simulations are essential for exploring the kinetic properties of physico-chemical systems, yet their practical application is often constrained by the overwhelming computational demands of high-dimensional systems. To make these simulations more tractable, dimensionality reduction techniques are employed to focus on the most relevant degrees of freedom, effectively shrinking the size of the system under study. However, this reduction introduces memory effects, where the dynamics of the system become dependent not only on its current state but also on its past evolution. These effects emerge because the discarded degrees of freedom—considered as part of the environment—continue to influence the system through delayed interactions. Addressing this challenge is critical: reducing the dimensionality of simulations while preserving their accuracy requires a robust framework to capture how the environment’s influence can be represented as colored noise, which accounts for temporal correlations in the system’s behavior.

This project will primarily focus on the numerical analysis and implementation of noise extraction from atomistic systems, aiming to model the environment’s influence as colored noise. The study will involve developing and testing computational methods to characterize and incorporate memory effects into reduced-dimensionality simulations. Several test cases will be examined, including ionic systems, pores in minerals, and crystal interfaces, to assess the robustness and generality of the approach. By analyzing these diverse systems, the project seeks to demonstrate how memory effects and colored noise can be systematically integrated into simulations. The results will provide insights into refining computational strategies for large‑scale molecular simulations in materials science and computational physics.

The project will involve substantial numerical development, including algorithm design, implementation, and validation against atomistic simulation data.

Where to apply

E-mail [e-mail]

Requirements

Research Field Chemistry Education Level PhD or equivalent

Research Field Computer science Education Level PhD or equivalent

Skills/Qualifications

The ideal candidate should hold a PhD in Mathematics, Physics, Chemistry or a related field with a strong computational/mathematical background. We are particularly interested in candidates with experience in one or several of the following areas:

• Inference of stochastics dynamics
• Numerical analysis / Mathematical analysis / modeling for chemistry or materials science,
• Machine learning force fields / data‑driven techniques for chemistry or materials science

Specific Requirements

This position involves a significant amount of numerical code development. Therefore the candidate will have prior scientific programming experience combined with an enthusiasm for code development.

Selected candidates will be contacted for an interview.

Work Location(s)

Number of offers available 1 Company/Institute Université de Caen Normandie - GREYC research centre Country France City Caen Postal Code 14000 Street Boulevard maréchal Juin Geofield