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Field
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System (W-0046 | all genders) with the freedom to develop and pursue your own research program within a broad scope. As a successful candidate, you will become an integral part of the Multiscale Cloud
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19.05.2025 Application deadline: 31.07.2025 The group “Physics of molecular and biological materials” at the University of Tübingen is searching for Post-doctoral researcher in Physics (m/f/d, E13 TV-L, 100%) The position is limited for two years. The research group of Prof. Dr. Dr. h.c. Frank...
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The INM – Leibniz Institute for New Materials in Saarbrücken, Germany, is an internationally leading center for materials research, a scientific partner to national and international research institutions, and a research and development provider for numerous companies throughout the world. The...
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field Strong publication record and experience in basic science or translational research Background in molecular oncology, tumor biology, liver-focused or immune-focused research, or computational
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within a joint research programme. What will be your tasks? The position focuses on the co-design and co-development of a monitoring assessment and policy framework for mitigating changes in marine
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26.02.2025, Wissenschaftliches Personal We are looking for a postdoctoral researcher (f/m/d) with a PhD in Simulation Technology, Computer Science, Mechanical Engineering, or a related field. About
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part of the School of Computation, Information and Technology (CIT) of TUM. The position is for 2 years and follows state regulations in accordance with the Collective Agreement for the Public Service
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to supervise PhD, Master and Dr. med (thesis as part of medical studies in Germany) students. The fellow will also have the opportunity to teach as part of the institute’s Masters and doctoral program but will
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and skills: You hold a PhD in Bioinformatics, Computational Biology, Genomics or a related field. You bring proven expertise in deep learning and statistical modelling of biological data. You have
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genomic approaches Application of the modeling approaches in relevant downstream tasks Co-development of high-performance computing AI training codes for the first European Exascale Supercomputer JUPITER