60 condition-monitoring-machine-learning-"Multiple" PhD research jobs in United Kingdom
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candidate will have strong analytical skills and substantial experience in machine learning at scale. The Prorok Lab in the Dept. of Computer Science & Technology, has a variety of robotic platforms (aerial
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/ . The post offers an exciting opportunity for conducting internationally leading research on the whole spectrum of novel machine learning algorithms and practical medical imaging applications, aiming
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We are seeking a highly creative and motivated Postdoctoral Research Assistant/Associate to join the Machine Learning Group in the Department of Engineering, University of Cambridge, UK. This
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of Cambridge. The position will be for 12 months from the 2nd February 2026 in the first instance. Project research topics must fall under the combined themes of human-computer interaction and critical
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participants Ideally, practical skills in one of (a) programming, (b) machine learning, and/or (c) design Responsibilities Developing and conducting novel research projects individually and on teams Developing a
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one of the following analysis techniques (multiple preferred): normative modelling, dimensionality reduction techniques, machine learning, deep-learning, state space modelling, advanced statistics
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into the genomics of population structure and speciation in the Malawi cichlid genus Labeotropheus. This post will build on past work in which over 1000 samples of Labeotropheus from multiple species/populations have
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to learn unfamiliar tools as needed (e.g. GitHub, APIs, new libraries). Familiarity with econometric techniques, especially panel data methods. Clear, concise written and verbal communication skills. Ability
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effect can be predicted. You will acquire in-situ and remote-sensing data of cirrus forming downwind of flights over the past decade, along with measurements/estimates of local conditions and emissions
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems