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are seeking a highly motivated and technical Senior Health Economic Modeller with expertise in Health Technology Assessment (HTA) to join the Liverpool Reviews and Implementation Group (LRiG). LRiG sits in
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production, exploring how nutrient imbalances affect plant physiology, yield, and crop quality. Working at the interface of plant science, sensor technology, and sustainable food systems, the PDRA will play a
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degree (or near completion) in Electrical Engineering, Computer Science, or a closely related subject. Research experience in wireless communications and deep learning is essential. The project will also
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friendly, proactive, helpful and approachable image. Working as part of a team, you will carry out day to day tasks as specified by your supervisor or Manager and have a flexible approach to working in all
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staff and students from across the Faculty of Science and Engineering, fostering interdisciplinary learning and ensuring an outstanding practical teaching experience. Commitment to Diversity
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willingness and aptitude for collaborating closely with the wider research team which includes chemists, engineers, industrial stakeholders and other major grants/hubs (EPSRC AI Hub in Chemistry (AIchemy) and
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technical priorities evolve and look to adopt new technology/approaches, where appropriate. Commitment to Diversity The University of Liverpool is committed to enhancing workforce diversity. We actively seek
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engineering materials. We are looking for a Postdoctoral Researcher with specific expertise to join the team and rapidly lead the development of a strategically important area of applied research. Collaboration
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dissertations and acting as an academic advisor. Depending on experience and expertise, you may also be asked to contribute to lecture-based modules in humanities, technology or professional practice. Suitable
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. Areas of interest that may be investigated include, but are not limited to: outcome assessment (including core outcome sets), use of digital technology, digital exclusion, use of healthcare systems data