55 postdoc-in-thermal-network-of-the-physical-building PhD positions at Technical University of Munich in Germany
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embedded into the quantum technology network formed by WMI, the Excellence Cluster MCQST (www.mcqst.de), the TU München (www.tum.de), the EU Quantum Flagship project QMiCS (qmics.wmi.badw.de), and several
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learning to push our understanding of the robustness and explainability of Federated Learning models. Your responsibilities: Build and create clinical use-cases for benchmarking existing state-of-the-art
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applicant must have the following: • Masters’ degree in Electrical Engineering, Mechanical Engineering, Physics or a related discipline • Experience with electronic circuits design and testing
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companies from all over the world, especially the USA, the UK, and Germany. Your Profile: Excellent university degree in engineering, chemistry, materials science, physics, electrochemistry or a similar
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personal development •Interdisciplinary networking in German and international wood and aroma research •Applications from women are explicitly encouraged. Preference will be given to candidates with
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reactor concept is developed with two other research groups at the TUM Campus Straubing who specialize in electrochemical processes. The final goal is to build a demonstrator on the mini-plant scale
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Program if you aspire to an academic career. We offer you access to an international research network by presenting your research at leading international conferences and spending a research semester at top
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have a strong interest in process-oriented research in plant physiology/plant ecology. Practical experience in field work, statistical data analysis (R), knowledge of German and a driver's licence are
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a defined research topic to deriving an improved process understanding and communicating the results. Ideally, the applicant should have some background in (soil) incubation experiments and advanced
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architectures which leverage our increasing understanding of the behaviour of neural networks trained with DP to ameliorate these trade-offs in biomedical applications. - Foundations of private machine learning