192 data-"https:"-"https:"-"https:"-"https:"-"UCL" positions at University of Nottingham
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our HR Shared Services team. This is an exciting opportunity to lead a specialist team responsible for maintaining and managing establishment data within the University’s HR Information System (HRIS
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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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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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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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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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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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applications sent directly to this email address will not be accepted. Interview date would be in week commencing 23 February 2026. Further information is available in the role profile. To apply for this vacancy
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facilities management Strong interpersonal and communication skills A proactive, solutions-focused approach Financial and data management capabilities A commitment to continuous improvement and inclusive
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will not be accepted. Interview date would be in week commencing 09 February 2026. Further information is available in the role profile. To apply for this vacancy please click ‘Apply Now’ to complete
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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