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Field
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time role, 0.1FTE. The activities of this role will support development of future proposals for funding, into AI for renewable energy. You will consider ways in which the integration of machine learning
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. You will also be responsible for implementing the model as a computer simulation and analysing it within a health-economics framework using standard computational techniques. You will also be
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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, Information Science, Software Engineering, Data Science and Business Analytics. At the postgraduate level, we offer research master’s and PhD opportunities – including specialist taught masters in Artificial
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the following training will be considered PhD in computer science, machine learning, AI or related computational field, or, Ph.D. in a health-related discipline with experience in experimental science, devices
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(EHR), health information exchanges, and data analysis software. Experience with health IT innovation, including working with artificial intelligence, machine learning, telemedicine, or mobile health
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apply cutting-edge machine learning algorithms, with focus on foundation models and LLMs/agents, to analyze complex biological data. This data includes gsingle cell genomics profiles, spatial data, and