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of precision fermentation or cell culture - Affinity towards technical tasks and bioprocess control - Advantageous: Experience in mathematic modeling, programming, CAD, microfluidic or bioreactor systems
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-constrained optimization problems, since the physical processes in the subsurface are governed by PDEs. In order to solve these mathematically challenging problems efficiently, new optimization approaches need
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on developing algorithms and foundations for deep learning and foundation models, particularly for medical imaging and on establishing mathematical and empirical underpinnings for machine learning. We
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economics, mathematics, business/management, physics, psychology, IT, or related fields • Outstanding quantitative skills (profound knowledge of stochastics and calculus is mandatory; knowledge of game theory
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Degree or equivalent Skills/Qualifications M.Sc. degree in engineering, applied mathematics, computer sciences or related fields. Excellent analytical skills, strong interest and capabilities in
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the group at TU Munich see https://www.cs.cit.tum.de/en/dss/ You have: - a degree in Computer Science or Mathematics. - an interest in optimization, game theory, and online learning. - excellent communication
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analysis (TEA) or an affinity towards these research questions. - Basic knowledge in bioprocess design, bioengineering and/or mathematic modeling - Affinity towards research question in life cycle
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of AI. The ideal candidates will have a background in computer science, statistics, mathematics, or related fields, as well as an interest in social science research methods and theories. The PhD
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, Mathematical modelling, Biology, Environmental Science, Physical Geography, or related fields. • Specific research in agricultural-environmental disciplines, and particularly with respect to livestock
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candidate, you have an outstanding Master's degree or comparable degree in biology, physics, applied mathematics or related disciplines. As an experimental candidate you have experience in biological systems