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- Technical University of Munich
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- Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung
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degree in Computer Science, Artificial Intelligence, Software Engineering, Robotics, or a related field Strong experience in software development and excellent programming skills, preferably in Python
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 10 days ago
the development of cardiometabolic diseases. The PhD position is based in the Research Unit and will be part of the Integrated Research Training Group (iRTG) of the German Research Foundation-funded Collaborative
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cardiovascular disease. A possible direction for the work is the development of probabilistic graphical models that represent proteins as latent causal drivers of disease, while treating other omics layers as
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of energy carriers to the degradation of persistent environmental pollutants and the development of more selective and environmentally friendly approaches in chemical synthesis. Here, we will advance isotope
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& evolution Proteostasis & phase separation Within these projects, you will have the chance to work closely with top scientists, learn valuable skills and get training in the latest techniques. See our research
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | about 1 month ago
://www.mardata.de/ . Your Tasks Development and application of advanced data science methods, including transformer-based models, to marine and Arctic biodiversity data Work with large-scale biological and
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, interdisciplinary environment and equips researchers with highly competitive skills spanning fundamental physics, technology development, and industry. Requirements: We seek an outstanding and highly motivated
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information is contained in these data and develop the computational and statistical approaches needed to extract it. Working closely with experts in imaging technology, algorithm development, biology, and
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on massive datasets that exceed the capabilities of single-node systems. A key objective is the development of efficient iterative algorithms for training networks based on random features, which can be
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Development: Excellent further training, cross-disciplinary collaboration, and practical insights into interdisciplinary challenges at the intersection of research data management, data science, and artificial