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academic area such as applied mathematics, computer science, physics, biomedical or electrical engineering or similar disciplines. Good programming expertise (Matlab, C++, Python or equivalent) and
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Job Id: 10797 Limited to 2 Years | Full-time with 38.5 hours/week | German salary grade E 13 TV-L | The medical faculty in collaboration with the mathematics department of the University of Münster
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/m/x) at the Peter Grünberg Institute – Quantum Theory of Materials – PGI-1 at Forschungszentrum Jülich in line with the Jülich model to be appointed as Full Professor (W3) (f/m/x) for Theory
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willingness to engage in interdisciplinary cooperation as well as cooperation with existing research groups in mathematical physics, dynamical systems, data science, machine learning, control theory
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Student or Postdoc (f/m/x) in the field of Theory and Methods for Non-equilibrium Theory and Atomistic Simulations of Complex Biomolecules Possible projects are variational free energy methods
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between individual molecules and cells. The MPI‑CBG shares a research faculty with the Center for Systems Biology Dresden (CSBD ), integrating expertise in biological physics, mathematics, and computer
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W3-Professor or W2-Professor with tenure track to W3 for Theoretical Physics – Relativistic Astrophy
competence in teaching in this subject area. We search for excellence in gravitational physics and relativity theory, with research interests for example in general relativistic astrophysics, gravitational
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computational tools for predicting satellite features in XPS spectra of 2D framework materials. Your work will be based on the GW approximation within Green’s function theory. While the GW method reliably
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genomics, virtual cell models Graph-based neural networks, optimal transport Biomedical imaging, deep learning, virtual reality, AI-driven image analysis Agentic systems, large language models Generative AI
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and analysis of mathematical methods for novel imaging techniques and foundations of machine learning. Within the project COMFORT (funded by BMFTR) we aim to develop new algorithms for the training