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, with a particular focus on the iron and steel sector. By using TROPOMI observations with advanced machine learning techniques, the project will provide independent information on emission patterns and
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modeling, molecular interactions/energetics, and tools such as AlphaFold, Rosetta, or MD simulations. Solid programming skills (Python); familiarity with machine learning is a plus. For both profiles, we
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economy. As a postdoctoral researcher, you will unravel how silicon suppresses liquid copper infiltration at the atomic scale, using density functional theory-accurate machine-learned potentials and
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Functional Theory (DFT)-accurate machine-learned interatomic potential (MLIP), enabling predictive MD simulations capable of resolving atomic-scale LCI mechanisms with near-DFT accuracy Investigate how silicon
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T cell Antigen discovery recognize. Currently, the group consists of 1 phd student, 1 technician and 5 post docs. The group has many close collaborations with industry, academic partners including
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The Leiden Institute of Advanced Computer Science ( LIACS ) is looking for an excellent Post-doc researcher (1.0 FTE) with a background in Computer Science (or a closely related field) to join a
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T cell Antigen discovery recognize. Currently, the group consists of 1 phd student, 1 technician and 5 post docs. The group has many close collaborations with industry, and academic partners