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data synthesis. Their work will determine how urban features drive species diversity, how species diversity and urban features are represented in soundscapes and how these relate to human health and
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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 efficient. We develop new optimization
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The Professorship of Public Policy for the Green Transition (PPGT) focuses on designing and evaluating policies for the green transition worldwide. The group uses a variety of methods from automated data analyses
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habits and duration of grazing under contrasting soil and environmental conditions. You will collaborate with data scientists to integrate empirical and novel evidence to improve the understanding
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and satellite-based 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
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, and high-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
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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
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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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December 18, 2022 to bewerbungen.gcfe@mgt.tum.de. For further information about this position please contact Ms. Lena Vorsatz (lena.vorsatz@tum.de). As an equal opportunity employer, TUM explicitly
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document. The position is suitable for disabled persons. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance. Data Protection