32 operations-optimization PhD positions at Technical University of Denmark in Denmark
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that merge thermo-fluid dynamic laws, deep learning, and experimental data. A central goal is to overcome current limitations in TES operation and optimization, enabling discovery of new high-performance and
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framework across a suite of practically relevant optimization problems in public transport planning and airport operations. The broader project also includes research on heuristic methods, in particular Large
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some background in one or more of the following areas: Mathematical Optimization / Operations Research Reinforcement Learning, Machine Learning, and/or Multi-agent systems Game Theory Algorithms
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be applying methods such as sensitivity analysis, robust optimization, and stochastic modelling as you work on your project. You will be seconded with the Chalmers University of Technology (Sweden) and
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available sensor and meter infrastructure, affordable computational resources, and advanced modeling algorithms. MPCs excel in handling constrained optimizations and new operational conditions, whereas RLs
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at the interface between chemical engineering, data science, and semantic web technologies. The work will be tightly integrated with other digitalization activities at DTU Chemical Engineering. Responsibilities and
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with researchers at DTU and KTH, you will help develop an integrated decision-support system that: Uses real-time sensor data and AI models to assess risk scenarios. Dynamically recommends optimal
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energy storage molecules. Solvent effects on catalysts, substrates, and their interactions, on the ensuing conversion pathways, and on their branch-points will be explored. Your work will strengthen
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Method to analyze the single-photon source performance (PhD1). Optimize and propose new single-photon source designs overcoming these limitations to be fabricated by other PhD students (PhD1). Perform
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available sensor and meter infrastructure, affordable computational resources, and advanced modeling algorithms. MPCs excel in handling constrained optimizations and new operational conditions, whereas RLs