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Computing Applications group in CSD has an immediate opening for a Postdoctoral Research Associate to design, develop, and deploy machine-learning and high-performance computing workflows, algorithms, and
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inequalities and Sobolev-type spaces (with Hytönen and/or Korte), 3. Conformal deformations of metric measure spaces and/or general regularity and convergence for graph-based machine learning using stochastic
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results in the form of scientific publications and by participating in international conferences and workshops. Contribute to supervision of PhD, MSc and BSc students and other teaching activities
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Appointment Term: 1-2 years depending on performance and availability of funding Appointment Start Date: Summer/ as soon as possible Group or Departmental Website: https://sparklab.stanford.edu/ (link is
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or by car (parking available). Public transportation costs are partially covered. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR7104-MARMEN-001/Default.aspx Requirements Research
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section Participate in experimental campaigns at the European XFEL About you: PhD or equivalent experience in physics, engineering, or a related discipline is required Strong skills in numerical modeling
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Researcher (R2) Positions PhD Positions Application Deadline 2 Mar 2026 - 23:59 (Europe/Madrid) Country Spain Type of Contract Other Type of Contract Extra Information Linked to project grant Job Status Full
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computer assisted sperm analysis on over 1,000 males from the national Swedish breeding program. A two-year postdoc position is available for investigating the underlying genetic factors of male fertility in
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 17 days ago
Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 26-Feb-26 Location: Chapel Hill, North Carolina Categories: Academic/Faculty Computer/Information Sciences Internal
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disorder. This project investigates early neural markers of psychosis by integrating multimodal neuroimaging with genetic and transcriptomic data and applying machine-learning approaches to identify