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mentoring graduate students. In keeping with MAIS goals, these opportunities will involve the mathematical foundations of AI, as well as the use of AI systems for mathematical and scientific
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Engineering, Mathematics, Computer Science, or a related quantitative field A strong mathematical background, including familiarity with optimization, probability, and the analysis of mathematical models A
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mathematical, data-driven, and AI-based models for sustainable multi-energy systems. Designing optimization and decision-making methods for system planning, scheduling, and operation. Exploring
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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 2 hours ago
King Abdullah University of Science and Technology: Postdoc Positions: Computer, Electrical and Mathematical Science and Engineering Division (postdoc): Applied Mathematics and Computational Science
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advanced optimization models and solution algorithms for robust scheduling and routing of bulk carriers under operational uncertainty. We are looking for candidates with: Strong expertise in mathematical
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research project PRONTO at the intersection of applied mathematics, automatic control, and data science, at the Department of Mathematical Sciences. About us The Department of Mathematical Science
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duties Research at the Lie-Størmer Center (LSC) aims to explore uncharted territory at the interface of computational and pure mathematics, targeting foundational mathematical challenges relevant to the
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) Curriculum Vitae At least two reference contacts Copy of your diploma (if already in your possession) Transcripts of study results How to apply? Apply online via: https://jobs.idlab.ugent.be/en/ph-d-ai-driven-resilient-ran-optimization
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offers the opportunity to develop strong competencies in multi-agent systems, learning and optimization, while addressing a timely and high-impact challenge: the growing use of AI agents in
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features. No cookies to display. Reject Save My Preferences Accept Skip to content PhD student in Machine Learning with a focus on mathematical and statistical methods for uncertainty quantification