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Field
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Technology and Logistics and co-supervised by at least one additional professor plus an international tutor of the RTG Requirements:very good or good university degree in physics, meteorology, fluid dynamics
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measurements at on- and offshore wind farms and a high-performance computing cluster. Almost all our projects combine analyses at more than one of these infrastructures. Further information is available
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the research group on Scientific Computing and High Performance Computing. For more details, see https://www.peter-zaspel.de/ and https://hpc.uni-wuppertal.de . Prof. Dr. Michael Günther is professor in
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. Experience in data processing and visualization is an advantage. Experience in conducting incubation experiments and operating instruments for continuous measurement of gases dissolved in water would be
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well as willingness to take part in the organisation and execution of field expeditions at sea are required. Experiences with data processing and visualization are advantageous. We expect very good English language
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students are supervised by a supervisory panel consisting of a panel chair, a professor from the natural sciences and a professor from information/computer/mathematical science. The DASHH graduate curriculum
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) tailored to the structure and properties of NMR spectral data Supporting the development and application of a self-supervised learning framework for pretraining the foundation model Assisting in the large
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. The study adviser provides any necessary assistance with regard to courses and logistic matters. At the end of the PhD programme, a doctoral dissertation can be submitted either in a traditional format or as
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on Responsible Data Science. The PhD positions will be at the intersection of Data Science and Social Sciences and will focus on topics such as Explainable & Fair AI, AI Auditing, AI Alignment, and AI Safety in
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applied in close collaboration with our clinical colleagues for investigating visual motion in a group of healthy subjects. The successful applicant holds a Master’s degree (or equivalent) in a relevant