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focuses on developing advanced optimization and control strategies (e.g., deep reinforcement learning) for large-scale sustainable grids, to enhance overall system stability, flexibility, and resilience
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properties. In this project, we will apply machine learning and optimization algorithms in order to achieve the design of such nanophotonic structures. As a postdoc you will be part of the Condensed Matter and
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apply machine learning and optimization algorithms in order to achieve the design of such nanophotonic structures. As a postdoc you will be part of the Condensed Matter and Materials Theory division, a
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research in design and optimization of turbomachinery, reactive fluid dynamics, multi-phase and turbulent flows, innovative technologies for biomass conversion, neural network systems, and artificial
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the Department of Energy Sciences. At the division, we conduct research in various fields, including research in design and optimization of turbomachinery, reactive fluid dynamics, multi-phase and turbulent flows
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(LLM), and optimization algorithms. Collaborating with our team to transform research insights into practical, impactful solutions. Staying abreast of the latest advancements in ML, transformers, graph
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concept will showcase capabilities of an energy island to not only optimally supply its own energy demands and support the grids in normal conditions but also to securely and optimally supply its critical
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Do you dream of organic and holistic ways of automating the design, motion optimization and control of legged robots? With this postdoc position, you have the opportunity to be a part of the ongoing
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methods to improve steel recycling and minimize the reliance on primary iron, by optimization of steel processing. Steel scrap is not a uniform material but a very diverse group of materials with widely
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sequencing (WGS). We are now aiming to further develop our analysis pipelines and are looking for someone to work on bioinformatics development, with a focus on optimizing the analysis of SNVs, structural