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Optimal Transport for Optimization and Machine Learning Appl Deadline: 2026/02/04 11:59PM (posted 2025/12/19, listed until 2026/02/04) Position Description: Apply Position Description Doctoral student in
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in porous media. The researcher will contribute to the implementation of new physics (such as transport, reactions, and thermo-mechanical couplings), computational performance optimizations
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deployment enabling validation and demonstration of real-world applications. For more details, please view https://www.ntu.edu.sg/erian We are looking for a Research Associate to conduct numerical modelling
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Science, Industrial Engineering, or a closely related field by start date. Demonstrated expertise in optimization (e.g., convex/nonconvex, stochastic, combinatorial), probability and stochastic processes, numerical
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National University of Science and Technology POLITEHNICA Bucharest, Pitesti Branch | Romania | 13 days ago
to design, implement, and optimize numerical models is required. The candidate must be able to perform steady-state and transient simulations, analyze system stability and response to disturbances, and
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Your Job: Energy systems engineering heavily relies on efficient numerical algorithms. In this HDS-LEE project, we will use machine learning (ML) along with data from previously solved problem
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, such as pulse design or numerical optimization Background in data-driven or machine-learning approaches relevant to optimal control (e.g., model learning, reinforcement learning) What you will do Take
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National University of Science and Technology POLITEHNICA Bucharest, Pitesti Branch | Romania | 13 days ago
research activities in the field of signal processing, artificial intelligence and optimization of electrochemical systems for hydrogen production. The activity focuses on the development and validation
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-hardware Your Profile: Master and subsequent PhD degree in (astro-)physics, computer science, engineering, or other related fields, with a strong focus on numerical simulations Experience in the development
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prediction Integration of domain decomposition methods into the learning framework to enable efficient model parallel training Implementation and optimization of GPU-accelerated training pipelines Validation