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Carl von Ossietzky Universität Oldenburg | Oldenburg Oldenburg, Niedersachsen | Germany | 23 days ago
Status Part-time Hours Per Week 26 Offer Starting Date 17 Sep 2025 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a
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) and the German Academic Exchange Service (DAAD) since 2007. Under this CAS-DAAD joint programme up and coming young Chinese scientists from the University of Chinese Academy of Sciences (UCAS) and CAS
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” (funded by the German Research Foundation, DFG). PhD Researcher in Computational Linguistics (m/f/d, E13 TV-L, 75%) The position begins on 01 October 2025 and ends on 30 June 2029. The position is
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how the brain collects audiovisual sensory information to create a multi-sensory perception of the environment. The central question is what criteria the brain uses to de-cide whether sensory
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. The specific focus of the PhD projects will be tailored to the candidate’s interests and will align with the objectives of the aforementioned consortia. These projects work at the interface of the gut microbiome
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of science, education, culture, economics, society, technology and the environment. The school of Mechanical Engineering and Safety Engineering, Chair of Materials Science and Additive Manufacturing (head
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the objectives of the aforementioned consortia. These projects work at the interface of the gut microbiome, diet, and the immune system to investigate their roles in various diseases, including but not limited
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16.08.2023, Wissenschaftliches Personal The Chair of Computational Modeling and Simulation (CMS) at the Technical University of Munich invites applications for the position of a Research Assistant
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. The specific focus of the PhD projects will be tailored to the candidate’s interests and will align with the objectives of the aforementioned consortia. These projects work at the interface of the gut microbiome
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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training