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on the collective agreement for the public service in the federal states of Germany (TV-L) Contact and Application For further information, please contact Prof. Dr. Ulrike Cress via phone: +49 7071 / 979-209 or by
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, responsible and independent work Friendly and co-operative working atmosphere in a small team Thorough familiarization with new working methods and equipment State-of-the-art working conditions in a future
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theoretical and practical experience with machine learning methods, especially for training of machine learning potentials - development and utilization of ab initio electronic-structure methodology - previous
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computer aided methods. Qualifications and Experience • Outstanding academic degree in materials science, metallurgy, metal physics or similar degree • Excellent doctorate with focus on computational
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Physical Oceanography and Instrumentation, Marine Chemistry, Biological Oceanography, Marine Geosciences, and Marine Observations works interdisciplinary within a joint research program. What will be your
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methods, machine learning algorithms, and prototypical systems controlling complex energy systems like buildings, electricity distribution grids and thermal systems for a sustainable future. These systems
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available in the further tabs (e.g. “Application requirements”). Programme Description The programme enables academics from all over the world to undertake one-to-three-month research residencies
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methods for the design, verification, and test of circuits and systems for conventional as well as alternative and post-CMOS computing technologies. Besides that, we have successfully applied the methods
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for students. Requirements We require 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
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scientific career. About us TUM’s new Computational Pathology and Medical Machine Learning lab (*2021) develops methods of machine learning (ML) and artificial intelligence (AI) for the analysis of digital