173 computer-programmer-"IMPRS-ML"-"IMPRS-ML"-"IMPRS-ML" positions at University of Nottingham in United Kingdom
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Training Programme, those based within the Faculty of Engineering have access to bespoke courses developed for Engineering PGRs. including sessions on paper writing, networking and career development after
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for career progression. • Employee Assistance Programme and Counselling Service- 24/7 support. • Supplier discounts, travel, and reward schemes. • Staff Networks, and wellbeing activities and state
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deliver a programme of outreach and engagement work. You will create detailed, item-level catalogues for the collections working from box lists. You will also help promote the collections you catalogue
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leadership contribution to the running of the BMBS programme providing academic oversight of the Advanced Clinical Practice component of the curriculum. This is a 0.2 FTE role which can be undertaken flexibly
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research programme funded by the Academy of Medical Sciences Springboard award. This project aims to explore the role of these neighbouring glycoproteins in neurotrophin-mediated neuronal development as
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Programme. The NBRC is a partnership between Nottingham University Hospitals NHS Trust and the University of Nottingham funded by National Institute for Health and Care Research (NIHR). The mission
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the School of Medicine. The group is internationally recognised for its work in the application of Artificial Intelligence (AI) and Digital Cancer Screening and collaborates with major NHS programmes, academic
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, plus 5 additional university closure days and bank holidays. Employee Assistance Programme and Counselling Service- 24/7 support. Supplier discounts, travel, and reward schemes. Staff Networks, events
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recognised qualifications. Personal and Career Development support, and opportunities for career progression. Employee Assistance Programme and Counselling Service- 24/7 support. Supplier discounts, travel
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’ or ‘internationally excellent’. The highly research active SP Section comprises 13 permanent academic staff with research interests in Bayesian computational statistics and machine learning, uncertainty quantification