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mathematics, (theoretical) computer science, machine learning foundations, electrical engineering, information theory, cryptography, statistics or a related field. - Advanced knowledge of probability theory
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, biophysics, epi /genetics, (bio)informatics, and multimodal data analysis. In this project, we will elucidate the molecular mechanisms that enable sperm to sense their chemical and physical environment, with a
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of medicine and computer science at TUM, as well as the Munich Center for Machine Learning (MCML). It is a great place for interdisciplinary research between medicine and data science. We are looking for a Post
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04.12.2025, Academic staff We are looking for an Independent junior research group leader (m/f/d) with knowledge of data / AI science to set up and lead the AI Core Unit of the BioSysteM Cluster of Excellence as soon as possible. The position is initially for 4 years and can be extended until...
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Max Planck Institute for Heart and Lung Research, Bad Nauheim | Bad Nauheim, Hessen | Germany | 2 months ago
Job Code: 2026_08 Job Offer from January 16, 2026 The Max Planck Institute for Heart and Lung Research, W. G. Kerckhoff Institute, Bad Nauheim, Research Group Cellular Resilience (Group leader: Dr. Karin Ziegler) is seeking, at the earliest possible date, a Postdoctoral Research Fellow (f/m/d),...
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for the position the following: A Ph.D. in the field of Applied Mathematics, Computer Science, Computational Science and Engineering, or similar. Knowledge of numerics as part of a degree program. In particular
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– in both health and disease. To this end, we combine interdisciplinary research in molecular, structural, and cell biology as well as in physiology, biophysics, epi /genetics, (bio)informatics, and
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17.02.2026, Academic staff A doctoral position (75% TVL-E13) in numerical analysis is available at in the DFG-funded Emmy-Noether Junior Research Group of Dr. Muhammad Hassan on the Numerical Analysis of Electronic Structure Methods for Molecules and Materials. About the PhD Project: The goal of...
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offers a lively, international research environment with well-equipped laboratories and computational facilities. The DSMZ strives to increase the proportion of female scientists and encourage female
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Learning algorithms for EDA in terms of Computational Complexity, Performance Scores, etc. To learn more about our previous work, please check out our website ( www.cda.cit.tum.de/research/machine_learning