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and prepare your doctoral thesis Your Profile Your qualifications and technical background include: An excellent master’s degree or equivalent in computer science, data science, applied mathematics
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quantitative, data-analytic, and writing skills who are eager to apply these abilities to questions in regulatory science, pharmaceutical policy, and the evidentiary basis of medical product regulation
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discovery to further human understanding. Faculty on the RTG-MAIS are listed below: * Amy Cochran: Computational psychiatry, data science, and mathematical biology. * Jordan Ellenberg: Arithmetic algebraic
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embedded FPGA-based intelligence near seismic sensor arrays, you will explore methods to maximize prediction performance while minimizing data transmission and latency. Your key tasks include: Evaluating
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of atomic-resolution structural and spectroscopic data. Your Profile Completed qualification to enroll for the Master thesis in Physics or Materials Sciences, with a good track record Willingness to
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will be embedded in an interdisciplinary research environment combining environmental systems analysis, supply-chain research and data science. PhD Topic Description The PhD project will develop
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Max Planck Institute for Solar System Research, Göttingen | Göttingen, Niedersachsen | Germany | 23 days ago
Postdoctoral Positions (f/m/d) | SO/PHI data reduction and exploitation Scientist Göttingen Astronomy & Astrophysics Job Code: Sonne: Postdocs SO/PHI-Solanki Job Offer from September 09, 2026 The Max
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Doctoral student in physics-guided foundation model for time-series data Reference number REF 2026-0434 Join us to develop physics-guided, data-driven foundation models for multivariate time-series
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program in electrical engineering, energy systems, computer science, data science, applied mathematics, industrial engineering, or a related field, and have demonstrated very good academic performance in
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and Machine Learning , with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable