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Holloway, University of London. Successful applicants will be working under the direction of Dr. Matteo Sammartino on the EPSRC-funded "Verification of Hardware Concurrency via Model Learning" (CLeVer
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computing, AI, biomedical engineering or signal processing) or a higher degree (e.g. MSc) with substantial experience in multimodal machine learning with appropriate track record. Excellent programming skills
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computer science with specialisation in data science, machine learning or deep learning, or in Earth/Environmental Science with experience in applied data science, machine learning or deep learning. You also will
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Telescope. This research may involve the analysis of large datasets, the generation of new simulations and the utilisation of Machine Learning techniques. The PDRA will be expected to hold a PhD in
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details and apply Overview About the Role Applications are invited for a Postdoctoral Research Associate with experience in machine and deep learning to work on a research project funded by the Precision
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Bayesian networks, causal discovery/machine learning, Markov decision processes, decision theory, simulation Applicants are expected to broaden their knowledge in autonomous electromagnetic defence systems
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at the PDRA level must have a PhD in Computer Science or a related field. Candidates must have substantial knowledge in Deep/Machine Learning, Large Language Models (LLMs) Natural Language Processing and
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Science/Electronic Engineering (or equivalent). Applicants at the PDRA level must have a PhD in Computer Science or related field. Candidates must have substantial knowledge in Deep/Machine Learning, Audio and/or
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their own niche of professional training and development. Examples of projects from the lab include identification of cancer drivers in individual patients using machine learning (Mourikis Nature Comms 2019
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the opportunity to learn cutting-edge molecular biology. There will potentially also be opportunities to develop computational skills and to interface with the other population-based studies at QMUL