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focus on deep networks for solving inverse problems, learning robust models from few and noisy samples, and DNA data storage. The position is in the area of machine learning, with a focus on deep learning
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, or electrochemical CO2 reduction. To do so, the preparation of novel catalyst materials is of pressing concern. You will continue our research line around the preparation of novel catalysts based on mixed metal alloys
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these proteins in probiotic bacteria and experimentally test their activity Optimize protein designs based on experimental results Develop bacterial biosensors of intra- and extra-cellular analytes Active
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01.07.2025, Wissenschaftliches Personal The position is based within the research group of Deniz Kus, Professor for Representation Theory at the Department of Mathematics, part of the TUM School
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will develop into acute or chronic infection. Your expertise - PhD in life sciences, preferably (liver) immunology and/or viral hepatitis. - Experience in high-dimensional flow cytometry for phenotyping
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, and high-performance computing. It aims to improve the performance of the matrix-free finite-element-based framework HyTeG, in particular by techniques for data reduction through surrogate operators
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cooperation with the other scientists is a prerequisite. Your profile: You have a PhD, work experience and several publications in the field of solid oxide cells. In addition, fluent written and spoken English
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consortium-based tasks related to the 6G-Life project. Additionally, the methods and findings developed throughout the PhD track will be scalable and applicable to other research projects in MIRMI
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process A wide range of opportunities for further training and career development Remuneration for the position is based on pay group 13 TV-L. The contract is limited to 24 months. For further questions
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quarks at the accelerator experiments, based on new nonrelativistic effective field theories and novel lattice QCD calculations. Targets are all aspects of the XYZs: spectra, decays, transitions