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If you are eager to perform cutting-edge molecular modeling to accelerate the development of green and low-carbon hydrogen technologies, then this is position is for you. Job description You will
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for semantic search, interpretation summarisation, and interactive chatbot support for defining factual states and generating tax ruling applications. The project incorporates advanced NLP models, in-context
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for semantic search, interpretation summarisation, and interactive chatbot support for defining factual states and generating tax ruling applications. The project incorporates advanced NLP models, in-context
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for semantic search, interpretation summarisation, and interactive chatbot support for defining factual states and generating tax ruling applications. The project incorporates advanced NLP models, in-context
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. The research will focus on developing hybrid learning–control architectures that integrate model-based control and planning methods with data-driven learning approaches. Potential topics include safe
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for identifying concentration, floe size, geometry, and possibly stage of development. The plan is to build models so that radar measurements alone can be used to populate, as far as possible, the Stage
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are optimized based on plant performance and energy price fluctuations. The learned models and resulting data-driven control approaches will be designed to be more easily transferable between different
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hands-on and system-oriented; a closed-loop, verifiable prototype will be built, tested under realistic degradations and examined for different metrics such as latency, reliability, fairness and SLA
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laboratory microcosm experiments to investigate how different environmental drivers influence species responses, community dynamics and ecosystem stability. Analyse experimental and field data using state
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renewable energy use, energy security, and the reliable operation of hydro-dominated power systems. The project will focus on how AI can support advanced optimization models for hydropower and energy-system