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mass and extracted material and can thus provide engineers and decision makers with important information for safety and productivity optimization. Your mathematical, analytical and problem-solving
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Carolina is inviting applications of two postdoctoral associates in the area of mathematical foundations of data science, AI, Graph Theory, Optimization, Probability Theory in a broader sense. The candidates
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. Knowledge of renewable energy systems (hydropower, wind, solar) and their grid integration. Experience in mathematical modeling, optimization algorithms, and data-driven methods. Possess a strong academic
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are reaching the end of their automotive service life while retaining significant residual capacity suitable for second-life applications. To address the above challenges, the Postdoctoral Researcher will
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novel computing paradigms, providing a unique opportunity to contribute to mathematics with a clear technological impact. We are recruiting two complementary postdoctoral researchers to join this
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Classification Title: Postdoctoral Associate Classification Minimum Requirements: A doctorate degree in an appropriate scientific discipline. Job Description: Under the PI’s supervision, be
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, while postdoctoral applicants are expected to have a strong publication record in machine learning, artificial intelligence, optimization, or related areas. Excellent communication skills and fluency in
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Uppsala University, Disciplinary Domain of Science and Technology, Faculty of Mathematics and Computer Science, Department of Information Technology Are you interested in working with probabilistic
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Uppsala University, Disciplinary Domain of Science and Technology, Faculty of Mathematics and Computer Science, Department of Information Technology Are you interested in working with machine
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
performance using existing mathematical models. Specific activities by the postdoctoral researcher will include, but are not limited to, the characterization of granular sorbents for their physical and chemical