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Bath Mental Health Research Group and the study team, please see: https://bathmhrg.org/ Funding Information Up to £2000 a year for research costs and training, plus £300 a year for a travel or conference
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. Aim You will design, build, and evaluate a hardware-aware AI framework for cognitive 3D mapping. The work will bring together three complementary sensing streams: structured illumination for active
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geometries and combine common materials in new ways to build bespoke, miniaturised antennas using magnetic nanoparticle structures. Your designs will make it possible to integrate digital technology platforms
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observation, semi-structured interviews with affected women and healthcare staff, and policy document analysis. The research focuses on how women manage exclusion—such as engaging with weight loss interventions
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decisions. The project is funded by Eni S.p.A through the UK Energy Futures (https://ukenergyfutures.org ). research partnership, bringing together geoscientists, engineers, and social scientists to advance
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for this vacancy and to view further information about the role, please click 'Apply' above. For informal enquiries, please contact Professor Kathryn Lilley (https://www.bioc.cam.ac.uk/research/lilley ) via
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at the European Research University, Czech Republic in cooperation with ERUNI's European research colleges (London, Amsterdam, Rome, and Paris). Our platform model aims to create a structured research environment
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the university portal here: https://www.brookes.ac.uk/study/how-to-apply/applying-directly . Please include the following in your application: A cover letter A CV Details of two referees, at least one
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strong self-motivation. You will be supervised by Dr. Mengyan Zhang (https://mengyanz.github.io/), whose research focuses on sequential decision making and public health. Dr. Zhang has published in leading
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and structured interview responses; and observable interaction signals such as gaze direction, facial movement, posture, gesture and vocal prosody. The successful candidate will investigate how human