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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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to contribute to a collaborative, interdisciplinary research environment Are eager to learn and explore new ideas at the intersection of ML and optimization Can program efficiently in one or more languages
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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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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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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
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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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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