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are looking for a highly motivated and dynamic PhD student for a position in the Translational Neuroscience team headed by Prof. Rejko KRÜGER. The Translational Neuroscience team's focus lies in
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through molecular dynamics, simulations, and benchmarks Active Learning in Configurational and Chemical Spaces Integrate uncertainty-aware MLFFs into active learning frameworks Explore automated dataset
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dynamics and biomolecular condensates contribute to PD co-pathologies in human midbrain assembloid models. The work combines advanced imaging, molecular biology, and functional disease modeling. Key
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unique training environment to advance microbiome science through metaproteomics. The program addresses One Health challenges by integrating research on microbial mechanisms, microbiome dynamics in various
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datasets across broad chemical space Evaluate models through molecular dynamics, simulations, and benchmarks Active Learning in Configurational and Chemical Spaces Integrate uncertainty-aware MLFFs
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as part of the CET (Comité d’Encadrement de Thèse) and will be daily supervised by the Postdoc employed in the project by UL. The PhD candidate will join the Mobilab Transport Research Group, a dynamic
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apply ultra-fast machine-learning interatomic potentials (UFPs, Xie et al., npj Comput. Mater., 2023, 10.1038/s41524-023-01092-7 ) for long, multi-million-atom molecular dynamics (MD) simulations
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SD- 26053 PHD IN ULTRA-FAST MACHINE-LEARNING INTERATOMIC POTENTIALS FOR NANOINDENTATION OF TIC MA...
: • Experience developing machine-learning interatomic potentials • Experience with UFPs • Experience with molecular dynamics, ideally with LAMMPS • Contributions to a public code repository Your LIST benefits
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The successful candidate will pursue doctoral research in group actions, geometric structures, and smooth dynamics under the supervision of Prof. K. Melnick, within the Department of Mathematics
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Designing hierarchical graph‑based models to predict toxicity under uncertainty by linking molecular‑level and system‑level knowledge Advancing causal inference methods to predict transformation products