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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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include: Skills in mathematical modelling and machine learning of relevant physical glacier processes (ice sheet and mountain glaciers), with proficiency in MATLAB/Python/Fortran, and related software tools
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, including use of scientific libraries (e.g., NumPy, Pandas, Matplotlib, etc). Experience with machine learning (e.g., Scikit-learn, PyTorch) or physics-informed neural networks for thermal systems is a plus
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, TESPy, or similar libraries. Strong programming skills in Python or MATLAB, including use of scientific libraries (e.g., NumPy, Pandas, Matplotlib, etc). Experience with machine learning (e.g., Scikit
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piglets who are unable to acquire own mothers’ colostrum. The majority of the experimental in vivo work is located at Frederiksberg Campus, albeit but with some investigations under commercial farming
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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
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are part of a sub-project on Algorithmic Sustainability in association with Professor Christina Lioma and her Machine Learning research team in the Department of Computer Science at the University
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(e.g., based on physiological signals or direct inputs from occupants) and developing algorithms, including machine learning methods. The work will include statistical modelling, data-driven modelling
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the disparities. While foundation models offer great promise for creating more robust machine learning models for a wide array of tasks, it remains an open problem how to foresee their biases across that wide array
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considerations. Experience working with machine learning methods for control, perception, or decision-making in physical systems is an advantage. Knowledge of or a passion for sustainable computing