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to conduct multidisciplinary research around robot learning for autonomous robotic chemists, with a background of excellent research outputs across Robotics and Machine Learning, ideally with a background in
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navigation algorithms and machine learning models on physical robot platforms. We are particularly interested in candidates with expertise in generative AI and curriculum learning applied to robotics, as
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of the study. The post holder will be based at the University of Edinburgh’s Centre for Cardiovascular Science, a leading centre combining world-leading cardiovascular disease research, state-of-the-art machine
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to reconstruct subsurface defects; Implement image/signal‑processing or machine‑learning pipelines for automated flaw characterisation; Collaborate with the Federal University of Rio de Janeiro, including short
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on understanding the spread and control of human infectious diseases using modelling and pathogen genomics. This is a short-term opportunity to apply machine learning methods to two key projects. First, you will
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develop statistical and machine learning models to identify and validate predictive biomarkers of resistance evolution in Pseudomonas aeruginosa lung infection. As part of this work, the postholder will
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., knowledge representation and reasoning) and bottom-up (e.g., machine learning) methods to study the representation of geographic categories and processes. While we welcome applicants from a broad range of
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more effective screening and therapy. The postholder will focus on developing and applying advanced computer vision and machine learning methods for multimodal imaging and real-time analysis in
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Full time: 25 Hours per week Fixed term: 12 months We are looking for a candidate to join the University of Edinburgh to conduct research on Machine Learning, Reinforcement Learning, or LLM
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of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet and glacier models, based on large ensembles