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Field
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knowledge and experimental experience in X-ray photoelectron spectroscopy Suitability to work in a radiation protection area Ideally, experience with further methods in the field of surface characterization
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evaluation methods for vision models Design metrics to compare neural and behavioral data with computational predictions Collaborate with CRC groups on open, reproducible standards for model evaluation Mentor
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institutions, and a research and development provider for numerous companies throughout the world. The INM is a member of the Leibniz Association and has about 250 employees. The INM research group
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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
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Postdoctoral Research Associate in Sum-Frequency Generation Microscopy of Biomolecular Self-Assembly
strives to advance the capabilities of such nonlinear microscopy techniques and establish new methods for the elucidation of molecular structures at interfaces. As such, within this project
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environment in the fields of science, technology and administration as well as for the education of highly qualified young scientists. The computational imaging group at DESY is concerned with the development
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background in Statistical Mechanics and to mostly focus on analytical work and the development of novel quantitative methods for active matter, and to a lesser extent, on computer simulations of active fluids
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characterization of interface deformation mechanisms, including advanced SEM and TEM methods Finite Element Modeling to design and optimize micromechanical testing geometries Timely dissemination of research
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program during the postdoctoral training. We are looking for an enthusiastic scientist with a desire to work on a challenging and timely project using state of the art technology as part of a friendly and
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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