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program and full-time employment as a faculty member or researcher and therefore serve to further prepare an individual who was recently awarded a doctorate to undertake the responsibilities of a career as
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, computer science, or applied mathematics, and superb computer programming skills. Recommended skills include an advanced command of Python and a language in the C family. Ability and a strong desire to work in a
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distribution systems. The scholars will have a unique opportunity to work simultaneously on a real-world industry-grade microgrid and state-of-the-art Hardware-in-the-loop HIL testbeds. Essential functions
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seismic gap in the vicinity of the Istanbul urban area. This project is funded by the national funding program Deutsche Forschungsgemeinschaft (DFG) and focuses on the analysis of Distributed Acoustic
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. Expected distribution of duties includes: ● Laboratory benchwork: 75% ● Data analysis, writing, and presentations: 25% Qualifications Required Qualifications: ● A PhD degree in Neuroscience or a related
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of sparse matrix, tensor and graph algorithms on distributed and heterogenouscomputational environments. Basic Qualifications: A PhD in Computer Science, Applied Mathematics, Computational Science, or related
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Department of Computer Science of Faculty of Science invites applications for a POSTDOCTORAL RESEARCHER IN ALGORITHMS AND COMPUTATIONAL BIOLOGY starting from September 2025, or as agreed
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. Maintains compliance with good laboratory practice including the maintenance of adequate research records. Engages in open and timely discussion with their mentor regarding possession or distribution
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Max Planck Institute for Astronomy, Heidelberg | Heidelberg, Baden W rttemberg | Germany | 2 months ago
) spectral energy distribution fitting, star-forming regions/nebulae analysis, photometry, region morphology and/or catalog generation are in particular encouraged to apply. Applicants (m/f/d) should have a
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August 2025 Apply now Machine Learning models are increasingly important in the atmospheric sciences. After training, they can emulate model outcomes at a fraction of the computational cost of traditional