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About the Role Applications are invited for a part-time Research Assistant position within the Centre for Advanced Robotics (ARQ), School of Engineering and Material Sciences (SEMS) at Queen Mary University of London. The project is funded by Innovate UK and will be supervised by Dr. Ketao...
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(Maternity Cover) to support teaching on the mentorship programme and the evaluation of the online MSc Sexual and Reproductive Health Policy and Programming (SRHPP) which is co-delivered with the University
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and statistical modelling, statistical image analysis and computer vision, chemometrics, biophysics, bioengineering. Preference will be given to candidates with a demonstrated experience in applying
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to work on a project investigating mechanosensing in flies (Diptera). This post will focus on using detailed wing geometry models and free flight kinematic measurements in computational fluid and structural
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the College’s small animal referral hospital by further developing and delivering advanced cardiac surgical therapies through the open heart surgery programme, at the Royal Veterinary College. We are looking
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and development of the research programme. The successful candidate will undertake the research investigations under the supervision of the Principal Investigators and in collaboration with other
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Health Records Research (EHR) Group for an experienced epidemiologist/statistician to join an NIHR-funded programme of research (The INTEGRATE programme) in collaboration with the National Institute
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Programme on Gender, Growth and Labour Markets in Low-Income Countries (G²LM|LIC ). The post is for 10 months, from 1 September 2025 to 30 June 2026. The successful candidate will possess as many as possible
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About the Project We are seeking a talented and dedicated team of scientists, bioinformaticians and support colleaguesto join the ground-breaking PharosAI initiative – a £43.6M national programme co
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of computational and behavioural neuroscience with modelling and domestic chicks’ data. This position is funded by a Leverhulme Trust project entitled “Generalisation from limited experience: how to solve