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academic career and research. During the two-year appointment, SOAS Associate Lecturers will have the opportunity to gain experience in teaching and learning activities, to develop their research and/or
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challenge. This project builds on preliminary work using Reinforcement Learning for control strategies and aims to validate simulation results through wind tunnel testing. About you You should have: a PhD in
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, advanced statistical methods and the potential to develop pioneering reconstruction and calibration techniques involving machine learning. The PhD will prepare equally well for a career in industry and
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are essential. You should have expertise in at least one of the following areas: theoretical modelling, machine learning, artificial intelligence, or numerical methods for climate systems, and be confident in
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Machine Learning Approaches. You will have access to the excellent training opportunities at the University of Sheffield, and will spend time on site at Procter and Gamble. A range of highly desirable
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completion, preferably with an application of their research in a real-world setting. Coding and software engineering proficiency will be expected if relevant to their experience, e.g. for machine learning
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KTP Associate in Machine Learning ( Job Number: 25000811) Department of Computer Science Grade 7: - £39,105 - £43,878 per annum Fixed Term - Full Time Contract Duration: 30 months Contracted Hours
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- XXXXXBR_supporting_statement. ESSENTIAL CRITERIA Qualifications and Education 1. Postgraduate degree at PhD level in a related subject area and Postgraduate Certificate in University Teaching and Learning or equivalent
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comfortable communicating with PhD students, staff and external partners. You will possess excellent organisation skills with the ability to multi-task and prioritise your workload. You will be proactive and
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supported by cutting edge facilities including mass spectrometry, flow cytometry, sequencing, automation, scientific computing, bioinformatics, and machine learning. GBI will also have access to substantial