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Field
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: You will study the effects of privacy-preserving machine learning on memorisation, fairness, interpretability and model uncertainty utilising techniques from information theory and quantitative
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of information, methods and tools mitigate the identified obstacles. If you are Interest, please send your application as soon as possible but at latest until the 28 of February 2023 per Email to: kai.zosseder
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remote sensing data using High-Performance Computing at LRZ Publication of the results in scientific journals Assistance in teaching REQUIREMENTS: An above-average degree in mathematics, computer science
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Management Technologies are focusing on the design and evaluation of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more
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-performance computing. It aims to improve the performance of the matrix-free finite-element-based framework HyTeG, in particular by techniques for data reduction through surrogate operators. Furthermore, we aim
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contrasting soil and environmental conditions. You will collaborate with data scientists to integrate empirical and novel evidence to improve the understanding of functioning of tropical and subtropical
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we broaden our horizons! At our institute, you will work with software architects, user experience designers, requirements engineers, security experts, data scientists, psychologists, and sociologists
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the development of innovative solutions for real security and data protection issues. What you will do Many successful attacks on IT systems are based on exploiting vulnerabilities in software
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submitted in English via the application form on our website. An application consists of the following elements: personal data and funds requested outline of proposal (detailed requirements as to content and
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Heidelberg University and Stanford University, including population health researchers, clinicians, and methodologists. The researcher will lead analyses in large-scale electronic health record data