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. Work will emphasize the development and analysis of advanced methods in areas such as sparse signal recovery, compressed sensing, and statistical estimation, with a particular focus on their role in
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, approximation theory, high-dimensional probability theory, mathematical aspects of machine learning, compressed sensing Secondment: Univ.-Prof. Dr. Philipp Grohs (University of Vienna, Faculty of Mathematics
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sampling while minimizing the computational load of large-scale regional models? How much could compressive sensing and advanced regularization techniques help reduce the data volume that needs to be
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at least one applicant from each of these groups for an interview. If you fall into any of these categories, feel free to indicate it when applying for the position. Learn more about the criteria for being