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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
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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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architectures for foundation models The work will combine methodological development with large-scale experiments, aiming for contributions at leading machine learning and computer vision venues such as NeurIPS
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machine learning-based model to map satellite retrievals to ground based air pollutant concentrations Conducting error assessment on the derived concentration data Implementing new observational data
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The successful candidate will develop generative machine-learning methods for amorphous molecular thin films — the supramolecular structures that govern the performance of organic-electronic materials
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uncertainty, since misclassifications can lead to poor energy management decisions or inappropriate building automation responses. The goal of this project is to design and validate a machine learning framework
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 1 month ago
highly motivated AI Scientist (f/m/x) (best: Computational Pathologist / Machine Learning Scientist for Digital Pathology) to join our efforts in developing AI-driven virtual staining pipelines for cancer
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additional days off (e.g. between Christmas and New Year's) Flexibility: Flexible working time models, including options close to full-time , allow you to tailor your working hours to suit your individual
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methods You will work closely with: - Dr. Martin Ramacher (machine learning for environmental applications) - Dr. Matthias Karl (urban air quality modelling and emissions) and collaborate within a project
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modeling, and scientific machine learning to enable efficient simulations of complex molecular processes across realistic time and length scales. https://doi.org/10.48550/arXiv.2604.24245 https://doi.org