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of what criteria will be assessed at each stage of the recruitment process. Please note that this is a PhD level role but candidates who have submitted their thesis and are awaiting award of their PhDs will
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About the role: We are seeking a highly motivated and skilled part time (0.2 fte) Research Assistant to join the Oxford School of Nursing and Midwifery. This is an exciting opportunity to contribute
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Aalto University is where science and art meet technology and business. We shape a sustainable future by making research breakthroughs in and across our disciplines, sparking the game changers
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a postdoctoral researcher to work on a new project in the area of LLMs/multi-agent systems, under the direction of PI Professor Michael Wooldridge. The post is a full-time, fixed term position
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on and defensive mechanisms for safe multi-agent systems, powered by LLM and VLM models. Candidates should possess a PhD (or be near completion) in Machine Learning or a highly related discispline. You
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We are seeking a full-time postdoctoral researcher to join Torr Vision Group at the Department of Engineering Science (central Oxford). The post is funded by EPSRC and is fixed-term for one year
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Reporting to Professor Fernanda Pirie, the post holder will be a researcher on the project entitled ‘‘Tibetan law: the socio-historic exploration of a unique legal system”. The postholder will have
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, three PhD students and a postdoctoral researcher. The project is led by Dr. Yiren Zhao at Imperial College, who is supported by Prof. George Constantinides and Prof. Wayne Luk. The team also includes Dr
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programme investigating early lung fibrosis at King’s College London. It is anticipated candidates will have a relevant PhD in immunology or respiratory sciences, and have experience with cell culture
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Postdoctoral Research Associate in Forest Resilience, Climate Change, and Human Health in the Amazon
illnesses. The post holder will also co-supervise a PhD student who will be involved in the same project. This is a highly interdisciplinary project combining forest ecology, remote sensing, machine learning