197 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions at University of Nottingham
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working from home. This is a Part-time, Fixed term contract till 1st June 2026 - Monday to Friday - hours 8:45 a.m. to 5:00 p.m. Your working hours will be (21.75 hours per week). Further information is
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) initiatives, including cultural heritage events and equality impact assessments. Coordinate welfare support, ensuring students and staff can access timely pastoral help, information, and referrals to specialist
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for Postgraduate Research (PGR) students and staff associated with these three CDT programmes. The successful candidate will support financial monitoring, data management, and project reporting, as well as assisting
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. Unfortunately, we cannot accept applications from current registered students within the School of English because of the potential access to sensitive and confidential information. If you have any informal
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payments, maintenance of trial information in the databases and trial files. Your role will also entail arranging face to face meetings, teleconferences, investigator meetings, conference attendance, and
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the link to our benefits website What next - Further information is available in the role profile. To apply for this vacancy please click ‘Apply Now’ to complete your details. Your working hours will be
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A positive, proactive approach to service delivery and continuous improvement Additional Information This role is full-time (36.25 hours) and permanent Hybrid working is currently in place: a minimum
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progression Shortlisting is anonymous: we cannot see any personal data or the 'Additional Information' section in your application until shortlisting is completed. Shortlisting is by criteria-based questions
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attention to detail when making appointments, registering patients, inputting data, managing stock, assisting with reports and financial administration required for the smooth running of the clinic. Previous
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focuses on developing cutting-edge statistical/machine learning methods for fitting complex, multi-institutional network models to partially observed hospital infection data. This research will directly