Artificial intelligence for materials modelling towards responsible energy management

Euraxess; Lyon; Hybride; EUR 42 000 - 60 000; Plein temps

Résumé du poste

Université Lyon 1 – Institut Lumière Matière seeks a researcher to develop a research program in materials modelling powered by AI, focusing on nanostructured materials and energy-related interfaces. You will push machine-learning methods to accelerate quantum-level simulations while reducing computational costs.

You will contribute to teaching across undergraduate to Master’s programmes, including potential English-language delivery for selected courses, and collaborate with the MMCI team and

Qualifications

• PhD or equivalent in physics, computational physics, or a related field.

Responsabilités

• Develop a research program in materials modelling using AI methods.
• Advance machine-learning approaches to accelerate simulations and model complex systems.
• Contribute to teaching undergraduate to master’s courses and develop AI/ML curricular content.

Connaissances

Machine learning

Numerical simulation

High-performance computing

AI for physics

Formation

PhD in Physics

Outils

Python

TensorFlow

PyTorch

Description du poste

Organisation/Company Lyon 1 University - Institut Lumière Matière Department Physics Research Field Physics » Computational physics Researcher Profile Established Researcher (R3) Positions Other Positions Application Deadline 26 Oct 2026 - 16:00 (Europe/Paris) Country France Type of Contract Temporary Job Status Full-time 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

Summary of the scientific project:

The Institut Lumière Matière (iLM) is an internationally recognized research laboratory in physics and chemistry, combining fundamental research, innovation, and contributions to major societal challenges. It has developed integrated expertise in materials synthesis, characterization, and modelling, drawing in particular on numerical simulation and artificial intelligence.
The successful candidate will develop, within the MMCI team, a research program in materials modelling based on the development of artificial intelligence methods. They will develop machine-learning approaches to accelerate simulations and overcome current limitations in system size and timescale. The objective will be to model complex systems up to mesoscopic scales while retaining accuracy derived from quantum-level approaches. Research will focus in particular on nanostructured materials, van der Waals heterostructures, coordination polymers, and solid–liquid interfaces, with applications in energy-efficient electronics and in the storage, conversion, and sustainable management of energy.
The successful candidate will be expected to demonstrate the ability to conduct innovative research at the highest international level, secure competitive funding, and develop collaborations, particularly across the Lyon research ecosystem. They will establish their research within the MMCI team and may develop strong interactions with the DIAMOND platform of the DIADEM PEPR, thereby contributing to accelerating materials discovery.
The position lies at the interface of physics, materials science, numerical simulation, and artificial intelligence. The intelligent management of high-performance computing and the reduction of its environmental impact will require the development of advanced modelling methods capable of describing materials with quantum-level accuracy while limiting the energy consumption associated with computational workload.

Summary of the teaching project:
The Department of Physics has a strong policy aimed at supporting student success, based in particular on a competency-based approach, academic mentoring by designated faculty members, international openness, the development of professionally oriented programmes, and continuing education.
It offers a broad range of programmes in physics and related fields. The Department regularly upgrades its teaching laboratories, evaluates and develops its curricula, and contributes to interdisciplinary teaching.
The successful candidate will teach at all levels, from undergraduate to master’s programmes, within the courses coordinated by the Department of Physics, including lectures, tutorials, and laboratory classes.
Teaching may cover topics related to the candidate’s research activities, both at undergraduate level and within the Master’s programmes in Fundamental Physics and Applications and in Materials Science. The successful candidate may in particular contribute to the future “Quantum and Complex Light-Matter Physics” track. Part of the Master’s-level teaching may be delivered in English.
The successful candidate will also contribute to the development of courses on artificial intelligence and machine-learning methods applied to the physical sciences.