203 computer-science-programming-languages-"St"-"University-of-St"-"St" positions at Technical University of Munich
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extraction from remote sensing data in close cooperation with the Department EO Data Science of the Remote Sensing Technology Institute of the German Aerospace Center (DLR). For this international, exciting
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for geospatial data acquisition in which we support (in a project with partners from the computer engineering domain) the development of novel AI chips made in Germany by providing use-cases and investigating
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of living systems, hence, catalyzing breakthroughs in biology, medicine, and the environment. Comprising 11 inter-disciplinary laboratories and scientists from more than 25 countries, CBI offers state
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, computer science, mathematics, physics, or a related field with an outstanding academic record. Interest in mathematical signal processing, optimization, and/or machine learning is important. Since
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: M.Sc. in Computer Science, Machine Learning, or equivalent with interest in Medical Imaging and Deep Learning. Strong knowledge in Machine/Deep Learning with experience in discriminative models
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journals. Close collaboration with team members and colleagues. Essential qualifications: M.Sc. in Computer Science, Machine Learning, or equivalent with interest in Medical Imaging and Deep Learning. Strong
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, agriculture science or related fields (Science: Biology, Computer Science, Data Science, Agricultural Economics etc.; Engineering: Mechanical, Electrical, Bionics, etc.) 3. Programming knowledge for AI
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Immunology, Molecular Biology, or a related field (for postdoctoral fellow). • Strong background in B cell biology. • Hands-on experience with transgenic mouse models, including breeding and colony management
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and ability to express yourself in spoken and written German and English language • Proficient use of common programming languages, MS-office, data storage, and image processing • Ability to support the
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wellbeing. They will use modeling approaches to combine natural and social science data. You will focus on developing and implementing mixed modeling approaches to test pathways through which structure