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computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
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-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven models for complex data, including high
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fluids, flow-induced pattern formation in both simple and complex flows (e.g. flow instabilities, product defects), multiscale analysis, and the application of machine learning techniques. About the
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include absence due to illness, parental leave, appointments of trust in trade union organizations, military service, or similar circumstances, as well as clinical practice or other forms of appointment