49 cloud-computing Postdoctoral positions in Ireland-University-Ranking-2024 in Germany
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- Technical University of Munich
- Heidelberg University
- Free University of Berlin
- University of Potsdam
- Constructor University Bremen gGmbH
- Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association
- Leuphana University Lueneburg
- Saarland University
- Ulm University
- University Hospital Essen, Collaborative Research Centre 1752
- University Hospital Magdeburg
- University of Tübingen
- University of Würzburg - NUCLEATE Excellence Cluster
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14.12.2022, Academic staff The BMBF-funded position is part of the CoMPS project, which is a multidisciplinary project combining the fields of mathematics, computer science, geophysics, and high
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of medicine and computer science at TUM, as well as the Munich Center for Machine Learning (MCML). It is a great place for interdisciplinary research between medicine and data science. We are looking for a Post
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track record Above-average master’s degree in computer science, electrical/ mechanical engineering, applied mathematics, or a similar engineering-oriented quantitative discipline Advanced software
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systems in East Africa, and in the subtropics in Latin-America. The research programme will examine productivity of grasslands, nutrient stocks and cycling and their relationship to biodiversity. We conduct
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problems or machine learning more broadly. We are looking for candidates with strong mathematical skills and interests. A requirement for the position is a master’s degree in electrical engineering, computer
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Latin-America. The research programme will examine productivity of grasslands, nutrient stocks and cycling and their relationship to biodiversity. We conduct experiments in the field to understand how
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related field (e.g. battery technology, automotive engineering, electrical engineering) and have previously completed your master's degree in a technical field, such as mechanical engineering, computer
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are modeled using information theory. We wish to investigate how interleaving can reduce the overhead and computational load due to coding coefficients required in classical linear random network coding
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the following areas: PhD in mechanical/electrical engineering, robotics, computer science, or a comparable field, Experience in self-reliant managing of research projects (financial and administrative) and