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sophisticated machine learning models to infer the location of hidden or obscured conductors. You will work in SSEN’s core asset data team, working collaboratively to develop tools and embed techniques to develop
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and machine learning to the selection of appropriate technologies. Disseminate findings through peer-reviewed publications, workshops, and conferences. Contribute to project management, reporting and
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year-long module performance in the water industry; (ii) exploring whether machine learning, couple with transport informed models can be used to predict membrane fouling for specific applications
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are looking for a researcher with a PhD (or near completion) in engineering, data science, computational social science or a related discipline, with experience in data analytics, NLP or machine learning. You
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establishing yourself in the field. Candidate Profile: A PhD in Power Electronics is desirable or a minimum Masters level qualification with some industry experience (both in power electronics). Skills