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mechanics (QM / MM) molecular dynamics simulations using a Quantum Centric Supercomputing (QSC) approach [1,2] for the QM problem. This will be integrated within the in-house MiMiC framework [3] and applied
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! You will demonstrated expertise in developing machine-learning interatomic potentials (MLIPs) for large-scale molecular dynamics (MD) simulations of materials. Together, we will push the boundaries
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Dynamics is a vibrant and creative research environment. For us, teamwork is key. We share experiences and help each other to create the best possible science in a fun and inspiring research culture. Our
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on forests and forestry as complex socio-ecological systems. We closely collaborate with multiple stakeholders and conduct applied research in silviculture, forest ecology, pathology, policy and planning. We
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tasks for the position The candidate will pursue research on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case of molecular data in cancer