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interface of machine learning, deep learning, data science and applications in forest sciences. Together with the Director, you will further develop KIForst as a faculty-wide platform for methodological
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(Master's or University Diploma) in a relevant discipline (Computer Science, Aerospace Engineering, Mechanical Engineering, Physics or similar). Sound knowledge of the fundamentals of Machine Learning, as
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position within a Research Infrastructure? No Offer Description Area of research: Scientific / postdoctoral posts Job description:Staff Scientist / PostDoc – Machine Learning for Scientific Applications You
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of these approaches quickly, in a data-based and reliable manner. Your main tasks include: Method development: you compare and decide on methods of Machine Learning (ML) and Artificial Intelligence (AI), such as
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for a Professorship (W2) (5 years/tenure track) of Statistical and Machine Learning in the Life Sciences, combined with the lead of a research group Computational Statistics & Dynamical Systems for Life
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implementation and commissioning Knowledge of machine learning or reinforcement learning applied to control systems is a strong asset Experience with accelerator technology, specifically photocathodes is an asset
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, pathology and outcome data Multi-agent and predictive AI development: Develop machine-learning components for patient-trajectory modelling, recurrence and survival prediction, and integrate them
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for Philosophy of Machine Learning for Science (m/w/d) to commence as soon as possible. The tenure-track position is embedded in the Cluster of Excellence “Machine Learning: New Perspectives for Science
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19.08.2026 Application deadline : 15.11.2026 The Tübingen AI Center invites applications for two tenured full professorships (W3) in Machine Learning and Intelligent Systems (m/w/d), to start as
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semester hours per week (2 SWS). Participation is expected in the EMOS program, the Munich School for Data Science (MUDS), and the Munich Center for Machine Learning (MCML). Cooperation with the institute’s