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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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external partners, you will contribute to the production of high-quality analytical outputs and publish results in leading journals. The postholder will have substantial ownership over one or more core
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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