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(diploma, master's degree) in transport engineering or civil/electrical/control engineering or mathematics, or related study programs with a solid basis in optimization Description of the PhD topic
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adaptive, automated decision-making tools (e.g., traffic signal control, human–vehicle coordination, logistics optimization, route planning) using reinforcement learning in dynamic environments. Explanation
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Grid Solutions Ltd on behalf of GE Vernova. The project’s topic will revolve around advanced high-voltage power electronics design and control, addressing both academic and industry needs. HVDC
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Wetsus - European centre of excellence for sustainable water technology | Netherlands | about 1 month ago
(e.g., regeneration agents, acids, antiscalants), leading to high costs and environmental concerns. Our challenge is to develop a desalination scheme that produces high-quality water and multiple
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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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optimization. Experience with energy system modeling - ideally of large scale multiple country energy systems, PtX and renewable fuel production. Strong writing and presentation skills. A willingness and desire
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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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Department of Mechanical Engineering and the AI research group at the Vrije Universiteit Brussel (VUB) are looking for a PhD candidate to contribute to research on the optimization of a hybrid, laser-based
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affordable and durable long-duration energy storage. The approach is to use hierarchical structures, i.e. complex material layers that can be optimized to specific battery chemistries and flow phenomena from
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skills and a keen interest in data-driven research. Your role will be to apply the developed semantic infrastructure to concrete case studies—such as cross-unit scheduling, process optimization