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Your Job: Scientific and technical lead of a team focusing on machine learning and big data analytics in X-ray science Development and application of machine learning tools for X-ray data analysis
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for habitats and biodiversity. The three-dimensional circulation, its variability and systematic changes will be investigated using model and observation data, as well as the dispersion of Lagrangian particles
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Interactions in a Changing World” is an interdisciplinary research initiative of geoscientists, biologists, and computer scientists at the Universities of Tübingen and Hohenheim and the Senckenberg Institution
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Your Job: You will primarily engage in research on or with large scale AI models (such as open foundation models) and datasets necessary for their creation using the supercomputers available
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-omics data, using innovative computational approaches supported by validation through molecular and immunological assays. Your tasks and responsibilities: Analyze large cohorts of genomic, epigenomic
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, diagnostics and therapies and, conversely, using clinical observations to develop new research ideas. The aim is to deliver relevant medical benefits to patients and the population at large. Since 2021 the BIH
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Innovation department, we explore a core question: How can we use the latest methods, technologies, and data to drive successful product innovation? We specialize in developing innovation teams – from newly
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Max Planck Institute for Extraterrestrial Physics, Garching | Garching an der Alz, Bayern | Germany | about 3 hours ago
which are used at large telescopes (VLT, LBT, ELT) and aboard satellites (for example XMM-Newton, Herschel, SRG/eROSITA, EUCLID, Einstein Probe, NewAthena). Within the framework of ESA’s future large
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Orchestration and documentation of Deep Learning experiments Processing and visualization of large amounts of data Review of relevant literature and data Testing software and frameworks What you bring
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the amount of tedious and error-prone manual work. The driving factor is the availability of large amounts of data. However, in this domain data is often sparse or labelling data is expensive. This requires