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Machine Learning for Acoustic Sensing under Uncertainty , done in collaboration with Aalto University and funded by an internal DTU grant. Our research at the Acoustic Technology group, DTU Electro focuses
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advantageous: Familiarity with LCA modelling, with consideration of mass and substance balances, data quality assessment, sensitivity and uncertainty assessment applied to membranes or similar products
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Computer Science section of DTU Engineering Technology and the Technical University of Munich (TUM). The project will investigate how technological, regulatory, and market uncertainties shape SMEs
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offer computational efficiency and the ability to handle uncertainties. This project aims to identify promising RL-MPCs for application and upscaling in real-life building management settings. As PhD
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accounting for uncertainties in the condition assessment Preparation of scientific publications and effective presentation of the research findings in international conferences, workshops, and training schools