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Vision concepts, including image processing, machine learning for visual inspection, and AI, within a manufacturing environment. Your work may also involve developing novel demonstrators for Computer
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low-progression backgrounds, refugees, care-experienced learners, and those with military connections Use data and insight to evaluate impact, inform strategy, and continuously improve outreach activity
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: machine/deep learning, numerical modelling, statistics, optimisation, scientific computing • Ability to work across disciplines and collaborate with academic and industrial teams Desirable: • Experience in
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: Earth Sciences, Bioscience, Interdisciplinary Life and Environmental Science, Inorganic Materials for Advanced Manufacturing, Chemical Synthesis for a Healthy Planet,Statistics and Statistical Machine
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. You will then develop a predictive Machine Learning tool to support engineers in incorporating vegetated systems into design stage decision making. Finally, you will apply Life Cycle Analysis
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, Engineering, or a closely related discipline. You will be a materials or physical scientist with a strong track record in applying deep learning to computer vision problems, ideally within battery
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As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skills
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standard of customer service. Previous experience of using email and finance/stock computer software is an advantage. Further information This role is offered on a full-time, term-time basis (36.5 hours per
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machine-learning-based surrogate models to accelerate design and control workflows. This PhD studentship would suit candidates with backgrounds or interests in engineering, physics, applied mathematics
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Error for Off-Shore Wind Measurement Systems Dr Anthony Brooms Software Engineering, Machine Learning, Natural Language Processing, Large Language Models Dr Taolue Chen Static Program analysis and