74 web-programmer-developer-"LIST" "https:" "https:" "https:" "https:" PhD positions at Technical University of Munich
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academic supervision from Prof. Henkel. You will participate in the doctoral program of the TUM School of Management; after about a year, there is the possibility to apply for the School’s Academic Train-ing
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. Ledendecker at the HI-ERN in Erlangen. The department specializes in the development of metal-based inorganic catalysts aimed at advancing the global energy transition. Our research focuses on using various
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(https://soilsystems.net/ ), a Priority Programme (SPP 2322) funded by the Deutsche Forschungsgemeinschaft (DFG; German Research Foundation). Within SoilSystems, scientists from different disciplines from
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or electrolyzers, H2O2 production, or electrochemical CO2 reduction. To do so, the stability of newly developed catalysts is of pressing concern. You will continue our research line around stability assessment
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Africa’s livestock sector. This work will involve modelling analyses using methodologies to be developed by the candidate in collaboration with their supervisors. This is one of two positions being hired
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of extension (TV-L E13 75%) in a highly motivated team combing on equal footing experimental and theoretical research. Professional career development complementing your scientific research is offered via TUM
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to challenging questions in the field of computational material design, especially with the help of CALPHAD-based methods. For further development of our simulation environment (https://github.com/cmatdesign
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are looking forward to receiving your application documents, which include your CV, your list of publications, transcript of record, a motivation of your research interests (max. 1 page) and the contact details
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03.06.2021, Academic staff The Albarqouni lab develops innovative deep Federated Learning (FL) algorithms that can distill and share the knowledge among AI agents in a robust and privacy-preserved
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03.06.2021, Academic staff The Albarqouni lab develops innovative deep Federated Learning (FL) algorithms that can distill and share the knowledge among AI agents in a robust and privacy-preserved