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for materials discovery. Project description Machine learning opens up new opportunities to accelerate the discovery of next-generation energy materials by combining predictive and generative approaches. In
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, you will contribute to research-based teaching and the supervision of student projects. Skills in mathematical modelling and machine learning of relevant physical glacier processes (ice sheet and
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. Responsibilities: Conduct research in compilation, optimization, and analysis for time-predictable computer architecture. Co-supervise MSc and PhD students. Contribute to teaching and research proposal preparation
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systems Strong skills in data-driven analysis and modelling, simulation, control, and validation Familiar with modeling of PtX and storage technologies, model predictive control, machine learning
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thermal and/or thermochemical energy storage systems. Implementing and validating advanced thermodynamic models for performance prediction and optimization. Collaborating with experimentalists and industry
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/or high-temperature heat pumps based on power cycles. Design thermal and/or thermochemical energy storage systems. Implementing and validating advanced thermodynamic models for performance prediction
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Sustainability in association with Professor Christina Lioma and her Machine Learning research team in the Department of Computer Science at the University of Copenhagen. The sub-package focuses