Research assistant (m/f/d) with 75%part-time job limited to 4 years salary grade (Entgeltgruppe[...]

Research assistant (m/f/d) with 75% part-time job limited to 4 years, salary grade (Entgeltgruppe) 13 TV-L, FU reference code: WiMi_Clementi_HH_2025

Join to apply for the Research assistant (m/f/d) with 75% part-time job limited to 4 years, salary grade (Entgeltgruppe) 13 TV-L, FU reference code: WiMi_Clementi_HH_2025 role at Freie Universität Berlin.

The Clementi group in the Physics Department at Freie Universität Berlin seeks a doctoral researcher (75%) to work on developing and applying coarse-graining methodologies to study macromolecular dynamics, involving statistical mechanics, molecular simulations at various resolutions, machine learning, and experimental data.

Our group focuses on strategies to study complex biophysical processes over long timescales, employing data-driven methods for systematic coarse-graining of macromolecular systems to bridge molecular and cellular scales. We aim to integrate information from simulations and experiments, combining high-resolution structural and dynamical data with lower-resolution experimental data.

Job Description

• Conduct research and teaching on statistical physics and machine learning in physics.
• Apply specially developed approaches to define transferable force-fields using machine learning for various complex molecular systems (proteins and materials) at different resolutions.
• Use developed force-fields to simulate specific molecular systems in collaboration with experimental groups, addressing biomedical or industrial questions.

The candidate will develop and utilize machine learning methods (primarily graph neural networks) to design representations and transferable energy models for proteins and materials. This position offers an opportunity to develop your scientific qualifications (PhD/Doctorate).

Requirements

• Master's degree in Physics, Chemistry, Applied Mathematics, or related fields.

Desirable

• Fluent English (spoken and written).
• Strong background in statistical physics.
• Previous experience in theoretical and computational physics.