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challenges. This PhD project aims to advance the efficient, controllable, and optimized use of renewable energy by integration of advanced TES technologies (latent heat and thermochemical storage) in
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learning, or reinforcement learning) Have experience developing algorithms for combinatorial optimization problems Have knowledge about decomposition methods for mixed integer linear programs Are motivated
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technology, positioning your career for long-term success and global scientific impact. Your primary role will be to pioneer and optimize advanced electron-beam lithography techniques to demonstrate reliable
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for the green transition of the energy sector. Our research develops innovative digital tools and methods, combining cutting-edge AI, simulation, and optimization, to create smarter, more resilient, 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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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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optimization, and stochastic modelling as you work on your project. You will be seconded with the Chalmers University of Technology (Sweden) and Mærsk McKinney Møller Center for Zero Carbon Shipping (Denmark
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technology, positioning your career for long-term success and global scientific impact. Your primary role will be to pioneer and optimize advanced electron-beam lithography techniques to demonstrate reliable