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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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science and artificial intelligence concepts and tools to solve complex problems. Candidates will also be developing machine learning techniques and applying them at scale to specific projects with regular
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for implementing the model as a computer simulation and analysing it within a health-economics framework using standard computational techniques. The post-holder will also be responsible for writing up the findings
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developing ideas for application of research outcomes. This post also be linked to research activities linked to the Faculty’s research platforms such as the Power Electronics, Machines and Control Research
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Health Regulations. Assist in project resource requirements and managing student projects. Job Requirements: Have a PhD Degree in Computer Engineering/Computer Science/Electrical Engineering or equivalent
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developing machine learning or data science approaches for patient stratification and genetic association analyses using cardiac magnetic resonance imaging in biobank populations. Successful applicants will
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quantitative data analysis: applied machine learning, statistical analysis, and handling complex data. Programming skills in Python and R are essential Experience in applying computational methods to research