419 embedded-system "https:" "https:" "https:" "https:" "UCL" research jobs in Germany
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available in the further tabs (e.g. “Application requirements”). Programme Description Are you researching the social effects of digitalisation? Do you want more freedom for your project and are interested in
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(e.g. via machine learning) to qualitative analyses (e.g. via interviews) to support ambitious policies for climate and energy transitions. This position Green hydrogen is key to decarbonizing many hard
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young scientists worldwide to German research institutions. For more information, see here . The position is suitable for disabled persons. Disabled applicants will be given preference in case
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06.09.2023, Academic staff The Livestock Systems research group at the TUM School of Life Sciences is recruiting a postdoctoral researcher (m/f/d) to work on grassland restoration. The aim of our
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Technologies at TUM’s School of Engineering and Design is looking for a doctoral researcher (f/m/d) in the area of Collaborative Machine Learning for the Energy Transition. You are passionate about machine
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The Livestock Systems research group at the TUM School of Life Sciences is recruiting a postdoctoral researcher (m/f/d) to work on grassland restoration. The aim of our group is to improve the understanding
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27.12.2021, Academic staff The Chair for Renewable and Sustainable Energy Systems (ENS) at Technical University of Munich (TUM) is part of the TUM School of Engineering and Design and deals with
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18.10.2022, Academic staff The lab for Artificial Intelligence in Medical Imaging (www.ai-med.de) is looking for a Post-Doc. The task will be the multi-modal modeling of medical data. The lab
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available in the further tabs (e.g. “Application requirements”). Programme Description As part of the HessenFonds, the Hessian Ministry of Higher Education, Research, Science and the Arts provides
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networks for solving inverse problems, learning robust models from few and noisy samples, and DNA data storage. The position is in the area of machine learning, with a focus on deep learning for inverse