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Field
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combining state-of-the-art machine learning with physicochemical knowledge and molecular modeling. Representative publications from our group include: https://doi.org/10.1038/s41467-025-63947-5 https
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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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mathematics, optimization and operations research, mathematical finance, econometrics and financial economics, and applied probability theory, covered by the Section for Insurance and Economics
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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 4 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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agentic artificial intelligence (AI) methods that propose, prioritize, and interpret experiments, accelerating the discovery and optimization of catalytic materials for energy and sustainability
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thermodynamic and kinetic models into an alloy design framework (including material properties models) that uses numerical optimization methods to rapidly screen for promising compositions over vast multi
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) incorporating symmetries and constraints into neural network models. Of particular interest are candidates who have a background and/or interest in one or more of the following: optimization and optimal control
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Postdoctoral Researcher in Quantum Algorithms and Optimization for Life Science Applications Employer AALTO UNIVERSITY Location Finland (FI) Salary 4200€/Month Closing date 30 Nov 2026 View more
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welcome but is not required. Relevant areas may include, but are not limited to, probability and stochastic analysis, optimization and operations research, statistics, dynamical systems and control
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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