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

Freie Universität Berlin; Berlin; Vor Ort; EUR 40.000 - 70.000; Vollzeit

Zusammenfassung

An innovative research group at a prestigious university is seeking a doctoral researcher to explore macromolecular dynamics through advanced methodologies in statistical mechanics and machine learning. This role offers a unique opportunity to bridge molecular and cellular scales, utilizing data-driven techniques to enhance our understanding of complex biophysical processes. You will collaborate with experimental teams to address significant biomedical and industrial challenges, all while contributing to your scientific qualification. Join a forward-thinking environment that fosters academic growth and impactful research.

Qualifikationen

• Master's degree in Physics, Chemistry, Applied Mathematics, or related fields required.
• Strong background in statistical physics and computational physics preferred.

Aufgaben

• Conduct research and teaching in statistical physics and machine learning.
• Develop transferable force-fields using machine learning for molecular systems.

Kenntnisse

Machine Learning

Statistical Physics

Molecular Simulation

Data-Driven Methods

Ausbildung

Master's Degree in Physics

Master's Degree in Chemistry

Master's Degree in Applied Mathematics

Tools

Graph Neural Networks

Jobbeschreibung

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 focuses on defining and implementing strategies to study complex biophysical processes over long timescales. We utilize data-driven methods for systematic coarse-graining of macromolecular systems, bridging molecular and cellular scales. Additionally, we develop theoretical formulations to exploit the complementary information from simulation and experiment, combining high-resolution structural and dynamical data from computational models with the lower-resolution data from experiments.

Job Description

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

The candidate will develop and employ machine learning methods, primarily graph neural networks, to design representations and transferable energy models for proteins and materials. This position will support the development of your scientific qualification (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.