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Computational modelling of two-dimensional graphene-based materials School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Natalia Martsinovich Application Deadline
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laser systems. For more details, please view https://www.ntu.edu.sg/eee. We are seeking a Senior Research Fellow to lead the design and development of chip-scale laser systems for quantum sensing under
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7 Apr 2026 Job Information Organisation/Company West University of Timisoara Research Field Physics Mathematics Chemistry Researcher Profile First Stage Researcher (R1) Positions Postdoc Positions
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25 Mar 2026 Job Information Organisation/Company Bocconi University Research Field Economics » Business economics Mathematics » Statistics Researcher Profile Recognised Researcher (R2) Leading
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The »Grid-free Methods« department at the Fraunhofer Institute for Industrial Mathematics ITWM focuses on the modeling and efficient simulation of complex industrial problems for the design and
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: The Mathematical Sciences Department offers undergraduate, Master?s, and Ph.D. programs in pure, applied, and computational mathematics. Research across a variety of disciplines is strengthened through
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, pluralistic and socially engaged university. Our credo is 'Dare to Think' : we are a haven for courageous thinkers. With one of the most progressive career models for academics in the world, Ghent University
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
reduction and manifold learning * High-dimensional inference and feature selection * Generative modeling and digital twins * Reliability and interpretability of ML methods The postdoctoral researcher will be
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California State University, Long Beach | Long Beach, California | United States | about 12 hours ago
Machine Learning, Mathematics Modeling, Artificial Intelligence, Data Science, High-Performance Computing, or Optimization Successful experience at the post-doctoral level or in industry, ideally including
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induced seismicity. Current models remain limited by the scarcity, heterogeneity, and noise of available data, as well as by incomplete knowledge of the subsurface. Physics-Informed Neural Networks (PINNs