62 evolution-"https:"-"https:"-"https:"-"https:"-"https:" positions at University of Newcastle
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pension schemes and a number of health and wellbeing initiatives to support you. Closing Date: 17 November 2025 The Role This is an exciting opportunity to join an inspiring education development team in a
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and input to applied health, public health and social care researchers on the design and delivery of research and the development of research funding applications. The NIHR RSS operates as a national
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that inform strategic decision-making, policy development, investment guidance, funding programs and improve alignment across agencies. We work across the health, life sciences and wider care ecosystem
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-class teaching, learning and research at our university. We are committed to expanding our knowledge and expertise with a strong focus on the training and development needs of our people. Your
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, actionable insights that inform strategic decision-making, policy development, investment guidance, funding programs and improve alignment across agencies. We work across the health, life sciences and wider
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researchers by delivering timely, actionable insights that inform strategic decision-making, policy development, investment guidance, funding programs and improve alignment across agencies. We work across the
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, actionable insights that inform strategic decision-making, policy development, investment guidance, funding programs and improve alignment across agencies. We work across the health, life sciences and wider
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the NIHR School for Public Health Research (SPHR) academic research training and development programme. The post holder works across organisational and professional boundaries, strengthening capacity
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Crystallography. The role will be centred around the development of a high throughput single crystal X-ray diffraction platform, compatible with previously developed high throughput nanodroplet crystallisation
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, data analysis, and modelling. This role will focus on the development, implementation, and validation of advanced ML approaches for weed detection, discrimination, and management using complex biological