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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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quantitative discipline, with demonstrated expertise in statistical model development and algorithmic methodology, particularly within Bayesian or probabilistic frameworks. You must have strong knowledge
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20 Dec 2025 Job Information Organisation/Company CNRS Department Institut de Recherche en Informatique Fondamentale Research Field Computer science Mathematics » Algorithms Researcher Profile First
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variety of cutting-edge research in time-domain astrophysics, including the development and implementation of machine learning, statistical and data-driven algorithms to study exotic transient phenomena
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(E13 TV-L, 100%, starting 1 April 2026 or as agreed) 22.12.2025, Academic staff The Professorship “Algorithmic Governance and Public Policy” (Prof. Daria Gritsenko), invites applications for a
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of machine learning. The position is part of the research project "Neural networks for homomorphic encryption", funded by Inria. Fully homomorphic encryption (FHE) enables computations to be performed directly
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- 00:00 (UTC) Country United Arab Emirates Type of Contract Permanent Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 8 days ago
Postdoctoral Program website for application instructions and requirements: How to Apply | NASA Postdoctoral Program (orau.org) A complete application to the NASA Postdoctoral Program includes: Research proposal
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Schedule: Monday – Friday, 8 a.m. – 5 p.m. Summary The Sedlazeck Lab is seeking a highly motivated Staff Scientist with strong computational and algorithmic expertise to lead and support cutting-edge
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disease progression. This includes integrating LLMs with structured data sources to develop robust computational phenotyping algorithms and scalable models for real-world evidence generation. The role will