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, including molecular clouds (properties, formation, evolution), dynamics (supermassive black hole mass measurements, gas flows, active galactic nucleus feedback), and any other facets of the data not yet
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principles that govern the motility, self-organisation and development of living systems. The post-holder will have the opportunity to teach. Applicants should hold (or be close to completing) a PhD in
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human tumour models including organ/tumour perfusion, slice culture and organoids to ensure data is clinically relevant and to inspire the next generation of effective treatments. The post would suit
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becomes essential. This project will focus on building a comprehensive digital twin of a future quantum computer to investigate how classical subsystems scale and interact, and how this scaling impacts
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team, and independently, are essential. You will also provide guidance to less experienced members of the research group, including postdocs, research assistants, technicians, plus PhD and project
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cutting-edge research at the intersection of RL and LLMs. You will also design and run experiments to improve LLM efficiency and sustainability. You will hold a relevant PhD/DPhil or be near completion
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of the research group, including postdocs, research assistants, technicians, plus PhD and project students. You must have: A relevant PhD/DPhil (or be close to completion), together with relevant experience in
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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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into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and applications (e.g. natural language processing, multivariate time-series data
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to determine the activators of inflammation in atherosclerosis. You will identify and develop suitable techniques, and apparatus, for the collection and analysis of data (e.g. flow and mass cytometry, confocal