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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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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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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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Tiny Machine Learning (TinyML). The role will focus on the design and development of battery‑less, ultra‑low‑power IoT systems capable of executing secure TinyML‑based visual perception algorithms
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language processing, machine learning and skills taxonomies, you will help generate meaningful insights into current and future engineering skills needs. Your work will support industry, policymakers, educators and
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that developments are well understood and embedded. To apply electrical/electronic design skills to create a prototype including circuit analysis, computer aided design and complex simulation tools, e.g. MATLAB/SPICE