121 algorithm-development-"Prof"-"Washington-University-in-St"-"Prof" Postdoctoral positions in Germany
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related fields, particularly in algebraic geometry and algorithmic algebra. For more information about the TUM Department of Mathematics, please visit our website: https://www.math.cit.tum.de/en/math/home
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. Are you interested in applying your machine learning and deep-learning expertise to develop cutting-edge ecological and environmental research? The Senckenberg Gesellschaft für Naturforschung invites you to
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project team on “Participatory Algorithmic Justice: A multi-sited ethnography to advance algorithmic justice through participatory design” (PARTIALJUSTICE) to examine issues of justice and participation in
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. The project’s overarching goal is the development of digital quantum algorithms for the simulation of non-abelian lattice gauge theories. We are looking for highly motivated individuals, with the desire
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algorithms into an existing framework, with a focus on efficiency, as well as creation and execution of relevant simulation pipelines: from real data to mathematical and clinically actionable results
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SEAGUARD This position is embedded in the SEAGUARD project (Seagrass Growth and Adaptation Using AI Research & Development), which focuses on assessing the CO₂ storage potential of seagrass meadows and
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biomedical sciences. What You Can Expect: We offer a diverse and stimulating range of tasks in the field of big data analysis, where you will develop and apply advanced computational methods to analyze complex
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near-real-time forecast system for the Baltic Sea Generate high-resolution daily surface salinity maps for the Baltic Sea and validate them with available observational datasets Develop algorithms and
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Your Job: We are looking for a postdoc to further develop probabilistic, statistical and game-theoretical methods for application in international nuclear safeguards. The tasks include: Connect
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to develop a 3D-generative algorithm for pharmaceutical drug design by using or combining novel machine learning approaches? How would you integrate machine learning, physics-based methods in an early-stage