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hardware-software prototype for Non-Intrusive Load Monitoring (NILM) that can provide real-time, interpretable energy-saving suggestions to households—completely on-device. This role involves applied machine
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one of the following analysis techniques (multiple preferred): normative modelling, dimensionality reduction techniques, machine learning, deep-learning, state space modelling, advanced statistics
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analytical ability with attention to detail Experience of analysing proteomic datasets Ability to work well as part of a team and rapidly acquire new skills Detailed subject knowledge in the area of
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simulate the impacts of extreme weather using future-looking climate-based projections. It aims to identify cost-effective, climate-resilient investment pathways for the GB’s whole energy system. The project
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-workers working on related projects • Be willing to learn new techniques and apply them in an interdisciplinary research environment in an autonomous manner • Be able to work as part of a team and