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Field
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tools and concepts. Supervise student projects and BSc/MSc theses. Your Profile: Master’s degree in physics, electrical/electronic engineering, computer science, mathematics, or a related field. Strong
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and develops a wide range of topics related to chemical hydrogen storage along the entire process chain. We place a particular emphasis on LOHC technology, addressing issues across different scales. Our
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equivalent) in computer science, data science, applied mathematics, physics, materials science, or a related field Prior experience in computer vision, deep learning, or signal processing; familiarity with
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, computer science, medicine, pharmacology, and physics. ISAS is a member of the Leibniz Association and is publicly funded by the Federal Republic of Germany and its federal states. At our location in
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the testing of newly devel-oped materials and the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission
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85748, Germany [map ] Subject Area: Physics / HEP-Phenomenology (hep-ph) Appl Deadline: none (posted 2024/08/04) Position Description: Apply Position Description Applications are invited for several Ph
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 8 days ago
in the IPK genebank: Your tasks: You work with elite × crop wild relative crosses to develop pre-breeding material that bridges research and applied breeding. You analyze experimental field data from
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Max Planck Institute for Extraterrestrial Physics, Garching | Garching an der Alz, Bayern | Germany | about 1 month ago
Job Code: 16/2025 Job Offer from July 24, 2025 The Max Planck Institute for Extraterrestrial Physics (MPE) in Garching is a world leader in the field of ground-based and space-based experimental
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knowledge in physical oceanography, climate change, Earth Observation, Big Data and data science as well as machine learning. This is an exciting opportunity to work on an exciting and ambitious project with
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
-based digital twin, which can predict the assortment and sequence of two-dimensional all-DNA motifs required for the engineering desired tessellation patterns at the nanometer scale. We will use available