235 assistant-professor-and-human-centered-computing Postdoctoral positions in Germany
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- Max Planck Institute for the Study of Crime, Security and Law, Freiburg
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Field
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and skills: You hold a PhD in Bioinformatics, Computational Biology, Genomics or a related field. You bring proven expertise in deep learning and statistical modelling of biological data. You have
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FIZ Karlsruhe – Leibniz Institute for Information Infrastructure is one of the leading provider of scientific information and services and a member of the Leibniz Association . Our core tasks are
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you can guide and mentor less experienced team members. If you are a team player. We offer you: The opportunity to work in a world-class research center with state-of-the-art facilities and scientists
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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 Bildungscampus Heilbronn (Heilbronn Education Campus). TUM Campus Heilbronn focuses on the areas of managing digital transformation, family businesses, and computer science. Requirements - Master’s degree in
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, computer science, mathematics, physics, or a related field with an outstanding academic record. Interest in mathematical signal processing, optimization, and/or machine learning is important. Since
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or instrument responsibilities Our Offer: We work on the very latest issues that impact our society and are offering you the chance to actively help in shaping the change! We support you in your work with: The
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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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at the newly founded Global Center for Family Enterprise (Prof. Dr. Miriam Bird). Expected starting date is April 2023 or by mutual agreement. The scientific employee (postdoc) will be employed on a 100
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communication system are modeled using information theory. We wish to investigate how interleaving can reduce the overhead and computational load due to coding coefficients required in classical linear random