64 postdoc-in-thermal-network-of-the-physical-building PhD positions at Technical University of Munich
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high-dimensional single-cell analysis and within the LPI network (scRNAseq, spectral flow cytometry) to translate fundamental insights into translational applications for human health and disease. We
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29.04.2021, Wissenschaftliches Personal This PhD position is in the field of resource management for wireless network that leverage “digital twins” modeling aspects of the physical (such as user
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normal physiology and autoimmunity in collaboration with project partners • elucidate the transcriptional networks regulated by FoxP3 and c-Rel in Treg cells and screen for novel critical regulators
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duration Payment is according to the wage agreement of the civil service TV-L, 65% of E13 for PhD student positions and 100% of E13 for Postdoc positions. Please note that there are no additional
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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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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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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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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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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