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geometry, number theory, data. * Qin Li: Scientific computing, machine learning, inverse problems. * Hanbaek Lyu: Combinatorics, probability, optimization, and machine learning. * Laurentiu Maxim
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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 2 hours ago
) for a collaborative project with a starting date of April 1, 2020. The candidate will be involved in the three-year project “High Dimensional Hierarchical Optimization methods for Machine Learning and
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work on the design, analysis, and implementation of innovative models for decision-making, economics, and operations. Research topics may include optimization, decision-making under uncertainty, market
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Job Description DTU Electro invites applications for a PhD position in AI-driven optimization of optical links based on nano-lasers, within the Villum Investigator program "Power-Efficient Fiber
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processing conditions Apply data-driven and machine-learning methods for multi-objective optimization of efficiency, reproducibility, and operational stability Study intrinsic material stability
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develop advanced optimization and analytics methods that improve routing, scheduling, and fleet utilization decisions while enhancing the robustness of operational schedules in the presence of uncertainty
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guarantees. Research areas include Representation Learning, Machine learning and Optimization on graphs and manifolds, as well as applications of geometric methods in the Sciences. This is a one-year
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electricity markets Practical relevance: You will work directly with a modern open-source model, and gain practical experience in developing, extending, and applying large-scale optimization models. You
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renewable energy. You will investigate quantum and hybrid quantum–classical methods for power system analysis and optimization, with emphasis on their practical performance and suitability for real
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learning; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing. Basic