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Field
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environment for reactors in serial production for operando studies Operando investigation of catalyst structures using high energy X-ray methods (scattering, spectroscopy, imaging) using in-house- and
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coding experience with e.g. Python/Matlab/R Practical experience with High Performance Computing, and scientific programming and a willingness to learn to work with high-performing computing systems
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or LibreOffice, Linux proficient in dealing with operating systems and high-performance computing facilities We expect: the ability to work independently and on your own initiative a methodical and systematic
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Office, Linux proficient in dealing with operating systems and high-performance computing facilities We expect: the ability to work independently and on your own initiative a methodical and systematic
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international researchers who would like to continue a promising research program in a highly stimulating research context that values ideas, innovation, and independent thinking. We offer the perfect
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us We are TUM’s unique Pathology AI lab developing new machine learning (ML) methods for automatically analyzing digital pathology data and related medical data. Such methods include the automatic
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of geoscientists, biologists, and computer scientists at the Universities of Tübingen and Hohenheim and the Senckenberg Institution of Biodiversity and Earth System Research in Frankfurt, investigating how
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experience through articles in internationally recognized refereed journals or working papers suitable for publication. You contribute in-depth knowledge of methods of applied microeconomics, experimental
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population health issues. Methods used in the group’s work include quasi-experimental techniques, descriptive epidemiology, and randomized trials. The fellow will be expected to publish in high-impact peer
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Excellent command of advanced statistical methods, such as network analyses, using the R programming language Strong publication record relative to the career stage, demonstrating research excellence and