27 condition-monitoring-machine-learning Fellowship positions at University of London
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exciting project that will develop new approaches to handle missing data in statistical analyses based on machine learning methods. The Research Fellow will be based in the Department of Medical Statistics
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, including seeking opportunities for mutual learning and training. The successful applicant will have a track record of relevant research experience, excellent communication skills, and an ability to work both
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have a postgraduate degree, ideally a doctoral degree, in a relevant field and experience in computer-based analyses and presentation of experimental data. The successful applicant would have a proven
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doctoral degree, in sexual and reproductive health or a relevant subject/discipline and a recognised teaching qualification equivalent to the LSHTM Postgraduate Certificate in Learning and Teaching, or to be
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relevant PhD and medical degree alongside registration with the GMC at Specialist Registrar Garde or below. You will have a recent track record in histopathology and use of machine learning techniques, and
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Weighting). The post will be subject to the LSHTM terms and conditions of service. Annual leave entitlement is 30 working days per year, pro rata for part time staff. In
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salary scale, Grade 6 scale in the range £45,907 - £51,156 per annum pro rata (inclusive of London Weighting). The post will be subject to the LSHTM terms and conditions of service. Annual leave
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£45,097 - £51,156 per annum (inclusive of London Weighting). The post will be subject to the LSHTM terms and conditions of service. Annual leave entitlement is 30 working days per year, pro rata for part
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postgraduate student learning through their specialty discipline. We are looking for candidates with an RCVS registrable degree in veterinary science, who also hold or are eligible to sit an American or European
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conditions of service. Annual leave entitlement is 30 working days per year, pro rata for part-time staff. In addition to this there are discretionary “Wellbeing Days”. Membership of the Pension Scheme is