296 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" research jobs in Germany
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The Bernhard Nocht Institute for Tropical Medicine (BNITM, https://www.bnitm.de/en ) is the largest Research Institute for Tropical Medicine in Germany with approximately 420 employees. It is the
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, statistics, machine learning) - a high motivation and the ability to work independently with a strong team orientation - excellent spoken and written English and the will to acquire a certain working language
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-good university degree in economics - strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) - a high motivation and the
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another related discipline, by the time employment starts Practical experience in machine learning, especially deep learning and its practical application in the domain of sensor analysis Practical
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radiation beamline hands-on experience in X?ray microscopy techniques, X?ray diffraction or X?ray fluorescence working knowledge of image processing e.g. using machine learning German skills For further
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four different AC-voltages, independent of the distance covered https://arxiv.org/abs/2108.00879 . Theoretical consideration indicate good prospects for maintaining spin-coherence of the shuttled
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for safety-critical bilateral teleoperation. The research will leverage a combination of passivity-based control methods and machine learning techniques to enable reliable and robust teleoperation in uncertain
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-body physics nonequilibrium quantum dynamics, to quantum computation, quantum information, and machine learning. The Institute provides a stimulating environment due to an active in-house workshop
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part of the Forschungsverbund Berlin (https://www.fv-berlin.de/) and part of the Leibniz Association (https://www.leibniz-gemeinschaft.de ). You can find more details on the institute webpage: https
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disabilities will be given preference over other equally qualified applicants. APPLY NOW Deadline: 9 April 2026 European XFEL GmbH Holzkoppel 4, 22869 Schenefeld, Germany www.xfel.eu Where to apply Website https