96 machine-learning-"https:"-"https:"-"https:"-"https:"-"U.S"-"U.S" PhD positions in Germany
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- Technical University of Munich
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- University of Tübingen
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- Max Planck Institute for Intelligent Systems, Tübingen, Tübingen
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- Catholic University Eichstaett-Ingolstadt
- Dresden University of Technology •
- Fritz Haber Institute of the Max Planck Society, Berlin
- German Cancer Research Center (DKFZ) Heidelberg •
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- Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association
- Helmholtz-Zentrum Dresden-Rossendorf •
- Helmholtz-Zentrum Hereon
- Humboldt-Universität zu Berlin •
- Johannes Gutenberg University Mainz •
- Justus Liebig University Giessen •
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- Max Planck Institute for Biogeochemistry, Jena
- Max Planck Institute for Dynamics and Self-Organization, Göttingen
- Max Planck Institute for the Study of Societies, Cologne
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- cellumation GmbH
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of different faiths and beliefs. Grounded in the Christian view of human life, the KU aims to create an academic and educational culture of responsibility. The research group Reliable Machine Learning at the KU
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with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) A high motivation and the ability to work independently with a strong team orientation Excellent spoken and
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qualified women. About the position The position contains both teaching duties and participation in research projects. The research project topics focus on improving object recognition through computer vision
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principles, kinetic Monte Carlo, machine learning) will be applied to investigate diffusion phenomena and link speciation with spectroscopic signatures. Formal requirements include a Master's degree in
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. Signal processing, AI, and sensor systems: You possess strong expertise in signal processing, particularly using statistical methods and machine learning / artificial intelligence techniques. You also have
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research on exciting projects and develop customised products and services for our clients from numerous industries and the public sector. The overarching topics at Fraunhofer ITWM are machine learning
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to environmental cues. Innovation drivers include the development of advanced technologies and the full integration of complex computational approaches to answer relevant biological questions. To learn more about
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), biostatistics, machine learning, data science and research data management, and causal inference methods (Iris Pigeot, Marvin Wright, Vanessa Didelez), and etiologic and molecular epidemiology (Konrad Stopsack
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: active learning (uncertain cases first), smart sampling, confidence thresholds, gradations (auto-label/review/manual), measurement and decision logic for throughput vs. quality. Proficiency in programming
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Nancy and the long-standing experience in sophisticated computer simulation studies from Leipzig, promising unique prospects in advanced education of PhD students via research into this important field