187 programming-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL" Postdoctoral positions in United Kingdom
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to demonstrate the ability to communicate effectively, plan and execute research activities, and work in a team. Teaching duties of up to three hours per week during University term time may be required. Only
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. Previous experience with dark matter direct detection experiments, or other rare event experiments will be an advantage. Candidates are expected to demonstrate the ability to communicate effectively, plan
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biological field. You must possess documented experience managing spatial/scRNA transcriptomics workflows. Strong programming proficiency in R or Python is essential. Proven experience in data acquisition
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demonstrations. The successful candidate will work at the intersection of multi-disciplinary modelling, advanced AI algorithms, and decision-support tool development. Responsibilities will include programming
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department in its field, running a globally competitive programme of research and teaching. The Kennedy Institute is a biomedical research centre uniquely bringing together discovery science and early-stage
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demonstrations. The successful candidate will work at the intersection of multi-disciplinary modelling, advanced AI algorithms, and decision-support tool development. Responsibilities will include programming
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programming methods in Python or similar languages, and evidence of working well as part of a multidisciplinary research team. Informal enquiries may be addressed to Professor Tingting Zhu (email: tingting.zhu
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Applications are invited for a Postdoctoral Researcher in Health Economics to work with Associate Professor José Leal and Dr Marjan Walli-Attaei on a research programme focused on the economics of
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: This post will contribute to a developing programme of quantitative research on work and well-being, in relation to modern work (flexible working, digitalisation of work, AI), leadership, and entrepreneurship
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Walli-Attaei on a research programme focused on the economics of cardiovascular disease prevention and chronic disease modelling. Funded by the European Society of Cardiology, the successful candidate