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Field
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) engineering, computer science, or a related field You have very good knowledge of energy technology, energy economics, and energy policy You have already gained initial experience in energy system modeling You
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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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highly motivated doctoral student to join an ambitious project aimed at building machine and deep learning models to study the genetics of human disease. Funded as part of the Helmholtz AI program, the
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Degree Doctorate Course location Potsdam In cooperation with The innovative PhD and postdoctoral scholarship programme is offered in conjunction with the Faculty of Digital Engineering, a faculty
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energy system model workflows Your Profile: Master’s degree in computer science, data science, natural sciences, economics, engineering, mathematics or a related field of study Huge interest in data
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Your Job: Analysis of possible applications of CO2 capture and CO2 utilization in the energy system requires detailed techno-economic modelling of these technologies. A major focus of this doctoral
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research program. What will be your tasks? This position will be part of the international SkaMix Consortium with partners from Norway, Sweden, Denmark, The Netherlands and Germany with the aim to identify
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xenograft (PDX) models. To this end, the candidate will have direct access to cutting-edge mass spectrometry and advanced bioinformatics infrastructure. The project is part of the collaborative research
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%, limited for 3 years, start: as soon as possible) in the trilateral program “Future Proofing Plants to a Changing Climate” (funded by DFG, UKRI-BBSRC, NSF, USDA-NIFA) Who we are: The research group Symbiosis
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now. As a result of this success and the dedicated community of plant enthusiasts, millions of plant observations are available, their numbers growing. These observations are useful for modelling