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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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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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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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welcomed. The project sits at the intersection of statistical genetics, systems biology, and machine learning, with strong emphasis on methodological development. Tasks of the PhD Student - Develop and
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Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. | Dortmund, Nordrhein Westfalen | Germany | about 12 hours ago
. or Diploma in bioinformatics or a comparable qualification Extensive programming experience Practical experience in machine learning and the application of large language models Knowledge of OMICS and image
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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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In addition to exciting tasks and a collegial working environment, we offer you much more: https://go.fzj.de/benefits Forschungszentrum Jülich is one of the largest research centers in Europe, with
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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