26 programming "https:" "https:" "https:" "https:" "https:" "https:" "https:" "https:" positions at UCL;
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: Postholders will undertake internationally excellent research as members of SOFAIR's interdisciplinary research programme, developing and testing original approaches to fundamental AI and contributing
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are interested in and undertaking work relevant to child health. GOS ICH holds an Athena SWAN Charter Gold Award. About the role We are seeking Research Assistant to contribute to an experimental programme
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(UASc) department is the home for the Bachelor of Arts and Sciences (BASc) , the MASc in Creative Health , the Bachelor in Creative Arts and Humanities (BACAH) and a doctoral programme in
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for around 250 students each year who plan to study an undergraduate degree at UCL or other Russell Group UK universities, but whose qualifications do not allow them direct admission. Academic content
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• Enhanced maternity, paternity and adoption pay • Employee assistance programme: Staff Support Service • Discounted medical insurance Visit https://www.ucl.ac.uk/work-at-ucl/rewards-and-benefits to find out
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Social and Historical Sciences. For more information see www.ucl.ac.uk/about This Post is for a Research fellow joining the UNESCO Chair in Disaster Risk Reduction and Resilience Engineering team, https
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programming (e.g. C/C++ on microcontrollers) for data acquisition and device control. You should have solid knowledge of analogue and mixed-signal circuit design for sensor interfacing and readout (e.g. low
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season ticket loan schemes, on-site nursery and gym facilities, enhanced family leave, an employee assistance programme, and discounted medical insurance. Visit https://www.ucl.ac.uk/work-at-ucl/reward-and
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the role To address this opportunity, CDB seeks to appoint an outstanding academic colleague who will establish an ambitious research and teaching programme applying artificial intelligence, machine
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Fellow to join an exciting interdisciplinary research programme. This is a unique opportunity to investigate mechanisms of cancer dormancy and develop predictive models of late recurrence in oestrogen