2 PhD Positions in Computational Modelling of Biological Membranes and Adaptive Functional Materials

University Muenster
Münster

How do molecular interactions give rise to biological function? How can materials acquire adaptive behaviour and process information? Although these questions arise in different scientific disciplines, they share a common challenge: understanding how complex behaviour emerges from the collective dynamics of many interacting building blocks.

The research group of Prof. Andreas Heuer at the University of Münster invites applications for two fully funded PhD positions in computational modelling of biological and functional material. The two projects address complementary questions in biological and functional materials while sharing a common scientific philosophy: using computational modelling to uncover the microscopic mechanisms underlying complex phenomena.

The positions are embedded in two Collaborative Research Centers (CRCs) funded by the German Research Foundation (DFG), providing a vibrant interdisciplinary research environment with close interactions between theory and experiment.

PhD Project 1 – Molecular Simulations of Asymmetric Biological Membranes (CRC 1348)

Biological membranes are intrinsically asymmetric molecular assemblies whose organization and function emerge from the interplay between lipids and proteins. Understanding how trans-bilayer lipid asymmetry controls membrane organization and protein function remains one of the central challenges in membrane biophysics. In this project, you will perform atomistic and coarse-grained molecular dynamics simulations to investigate asymmetric plasma membranes, protein–lipid interactions, membrane domain formation, and the role of sterols, sphingolipids and phosphatidylserine in lateral membrane organization. The simulations are closely integrated with advanced experimental approaches, enabling direct connections between molecular mechanisms and cellular observations. Where appropriate, modern structure prediction tools may support the construction of realistic simulation models.

The project is ideally suited for candidates interested in molecular simulations, membrane biophysics, physical chemistry, soft condensed matter or computational structural biology.

PhD Project 2 – Adaptive Functional Materials for Neuromorphic Computing (CRC 1459)

Adaptive materials capable of memory, learning and information processing represent one of the most exciting developments in modern condensed matter physics. Their remarkable properties emerge from the collective dynamics of many interacting microscopic processes, which remain only partially understood. In this project, you will develop and analyse physically motivated models for adaptive nanosystems. Using kinetic Monte Carlo simulations together with concepts from statistical physics and non-equilibrium dynamics, you will investigate charge transport, stochastic switching, memory effects and the emergence of neuromorphic functionality. The primary goal is to identify the physical mechanisms underlying adaptive behaviour and to establish predictive theoretical models. Surrogate models may be employed to accelerate simulations and explore large parameter spaces.

The project is particularly suited for candidates interested in statistical physics, computational physics, stochastic processes and theoretical condensed matter physics.

Research Environment

Our research aims to understand how microscopic interactions give rise to macroscopic behaviour in complex molecular and condensed-matter systems. Depending on the scientific question, we combine molecular simulations, statistical-mechanical concepts and theoretical modelling across multiple length and time scales. A hallmark of our work is the close interplay between theory and experiment, enabling microscopic simulation results to be directly linked to experimentally observable phenomena.

Your Profile

We welcome applications from highly motivated candidates with a Master's degree (or equivalent) in Chemistry, Physics, Biophysics, Materials Science or a related discipline.

Successful applicants should have strong analytical and quantitative skills, experience with scientific programming relevant for the project, enthusiasm for computational research and theoretical modelling, very good written and spoken English, curiosity about fundamental scientific questions and enthusiasm for interdisciplinary research.

We Offer

A fully funded three-year PhD position. Salary according to the German public service pay scale (65% TV-L E13). An excellent interdisciplinary research environment within two DFG-funded Collaborative Research Centers. Close collaboration with internationally recognized experimental and theoretical research groups. Access to state-of-the-art high-performance computing facilities. Opportunities for international collaborations, research visits and conference participation. Scientific independence, individual supervision and excellent career development opportunities

Application

Applications should include a motivation letter, curriculum vitae, academic transcripts, a brief summary of previous research experience and scientific interests, names and contact details of two referees, an indication of the project for which you wish to be considered

The anticipated starting date is November 2026 (or as soon as possible thereafter).

Please submit your application as a single PDF file to:

Prof. Andreas Heuer
Institute of Physical Chemistry
University of Münster
[email protected]

The University of Münster strongly supports equal opportunity and diversity . We welcome all applicants regardless of sex, nationality, ethnic or social background, religion or worldview, disability, age, sexual orientation or gender identity. We are committed to creating family-friendly working conditions. We actively encourage applications by women. Women with equivalent qualifications and academic achievements will be preferentially considered unless these are outweighed by reasons which necessitate the selection of another candidate.

Veröffentlicht am 2026-07-22

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