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position initially and is expected to be held full time and in person. You will join the CNNP Lab, which is well supported with recent funding of over £3M. The lab is based in the School of Computing
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work on the INSTINCT-MB programme, which brings together teams based at Newcastle University, The Institute of Cancer Research and University College London. The programme will generate a wide range of
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datasets held by the LRCG are richly annotated with patient demographic, clinical, treatment and outcome; and correlating genetics with clinical features is a major part of our programme of research. You
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an integrated programme of medicinal chemistry and further screening, PTSuppress will optimise these lead compounds and repurpose already approved drugs. Ultimately, we will validate the best compounds/drugs
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experience of high dimensional data analysis, including the ability to independently program in the R computing language. Please note that if you are successful in the role, you will require pre-placement
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collaborative team engaged in a range of research projects in marine hydrodynamics, both computational and experimental. This is a fully on-site role, with work taking place in the office and laboratory. We
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Document project progress, report results, and present findings to stakeholders Stay up to date with the latest machine learning techniques and tools, particularly in the field of computer vision The Person
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Research Assistant/Associate to undertake work as part of our NU Solve programme. Newcastle University Solve (NU Solve) in the School of Mathematics, Statistics & Physics (MSP) has a track record of
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responsibilities expected of role holders at each level and secondly the general qualifications and experiences needed for entry at a particular level. Key Accountabilities Contribution to the research programme of
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for 36 month fixed term at 0.6 FTE (22 hours). Key Accountabilites Organising intervention sessions and data collection, including set up, implementation and analysis of the SleepBoost intervention program