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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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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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have a PhD in a numerate field with expertise in machine and deep learning methods, supported by high quality publications. Experience of applying such methods to healthcare data is an advantage
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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 have a good track record
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quality research publications. About You The successful candidate will have PhD degree in bioinformatics, or significant post-doctoral experience with single-cell machine-learning methodologies. In
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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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competence in econometrics and machine learning are desired. Experience in economic experiments would also be desirable. Most of all, the successful candidate will have a great enthusiasm to conduct research