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are particularly interested in candidates with a strong mathematical background and expertise in one or more of the following areas: High-dimensional probability and concentration/functional inequalities Markov
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Join our team as a Early-Career PostDoc (m/f/d) in the research area of AI‑based modelling full-time (39.2 hr/week), fixed term for two years. Leibniz-IWT stands for research in the field of new
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Applications are invited for a Research and Innovation Associate in the atomic astrophysics group, Department of Applied Mathematics and Theoretical Physics, University of Cambridge. The successful
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, Mathematics, Physics, or a closely related field. Proficiency in machine learning libraries (e.g, scikit-learn, PyTorch, and transformers) and data analysis tools (e.g., pandas, NumPy, and CuPy). Hands
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physics, applied mathematics, machine learning, bioinformatics, biophysics, spectroscopy, image processing, ecological modeling, molecular biology, plant physiology, marine biology or an interest in gaining
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Postdoc: Geospatial AI Modelling & Uncertainty Quantification of Biomass Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 32 to 40 Application deadline
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 20 hours ago
process modeling) and laboratory wave tank experiments to explore turbulent seawater dynamics adjacent to ice. Opportunities may also exist to participate in planned field campaigns in Greenland
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-computer interaction, social networks, fairness, and data ethics. Our research is rooted in basic research and centres on mathematical models of the physical and virtual world, as a basis for the analysis
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techniques may be employed to support the modeling of uncertainty, along with the formulation and resolution of planning problems using stochastic optimization methods. Key Responsibilities The selected
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, Oxford, Leeds, Reading, and Birmingham) and international (Utrecht University, ETH Zurich, Université Catholique de Louvain, etc.) scientists to use new modelling resources and methods to elucidate drivers