51 machine-learning-phd-"The-Art-Institutes" research jobs at University of London in United Kingdom
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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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equivalent level of professional qualifications and experience, with expertise in at least one of the areas: machine learning, applied topology, and/or probability theory. About the School The School has
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meetings across the EU, perform R&D in the field of machine learning for plant species classification and support the implementation of the project application development. About You Applicants must have a
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areas: machine learning, applied topology, and/or probability theory. About the School The School has an exceptionally strong research presence across the spectrum of Mathematical Sciences. It is part of
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characteristics using hybrid machine learning approach. This project is in collaboration with Kyushu University, Japan under the supervision of Dr Amin Paykani at Queen Mary University of London to realise
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apply Overview About the Role Applications are invited for a post-doctoral research associate to lead on a Barts Charity funded project. The research involves application of machine learning methods
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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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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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emphasis of the project will be to investigate metabolic fate of apoptotic cell cargo following their ingestion by macrophages. About You Applicants should hold a PhD degree or equivalent in biological
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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