The Clementi's group in the Physics Department at Freie Universität Berlin seeks a doctoral researcher (75%) to work on the development and application of coarse-graining methodologies to study macromolecular dynamics with statistical mechanics, molecular simulation at different resolutions, machine learning, and experimental data.
Our group works on the definition and implementation of strategies to study complex biophysical processes on long timescales.
We use data-driven methods for systematic coarse-graining of macromolecular systems, to bridge molecular and cellular scales. We work on a theoretical formulation to exploit the complementary information that can be obtained in simulation and experiment, to combine the approximate but high-resolution structural and dynamical information from computational models with the "exact" but lower resolution information available from experiments.

Job description:
Application of specially developed approaches to define for transferable force-fields with machine learning for different classes of complex molecular systems (proteins and materials) and different resolutions. Use of the developed force-fields to simulate specific molecular systems in collaboration with experimental groups, to address questions of biomedical or industrial relevance. The candidate will develop and use machine learning methods (mainly graph neural network architectures) to design representations and transferable energy models for proteins and materials.
Contribution to teaching on statistical physics and machine learning. The position will serve to develop your own scientific qualification (PhD / doctorate).
The position will be funded by the Department of Physics of Freie Universität Berlin. It is limited to 4 years.

Requirements:
Candidates must have a Master in Physics, Chemistry, Applied Mathematics, or related fields.

Desirable:
Fluent English, spoken and written
Strong background in computational Physics and Statistical Physics
Previous work experience in molecular simulation