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. Employee Assistance Programme and Counselling Service- 24/7 support. Supplier discounts, travel, and reward schemes. Staff Networks, and wellbeing activities and state of the art sports facilities. Permanent
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. Personal and Career Development support, and opportunities for career progression. Employee Assistance Programme and Counselling Service- 24/7 support. Supplier discounts, travel, and reward schemes. Staff
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This post is available only for internal applicants (University of Nottingham staff only) Applications are invited to the above role to provide timely and high-quality technical support to teaching in the School of Life Sciences. The role holder will be required to work within the technical...
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- working during university term times: paid leave will be during Christmas, summer and Easter. Employee Assistance Programme and Counselling Service- 24/7 support. Supplier discounts, travel, and reward
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of technical skills in the disciplines of Exercise Physiology, Metabolism and Nutrition for the BSc Sport & Exercise Science programme. Based between the David Ross Sports Village and the Medical
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species, so requires the person to have clinical experience of working as a veterinary physiotherapist. The role will also contribute to teaching on the undergraduate veterinary programme. The post will
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. You will have: 1) Post-graduate level, ideally qualified with a PhD (or close to completion) in Building Services Engineering, Mechanical Engineering, Control Engineering, Computer Science, Data Science
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undergraduate teaching as part of the undergraduate Science Foundation programme in the School of Life Sciences. The primary purpose of the role will be to undertake teaching on the biology, chemistry and maths
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We are seeking an experienced data analyst to support immediate delivery of the Future Nottingham project. You should have the ability to innovate at pace and come up with data-driven solutions to emerging challenges and requirements in a fast and changing environment. Your main areas of focus...
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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