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and the economy. The postholder will be responsible for undertaking research in generative AI and machine learning methods for audio generation and audio-related multimodal content generation, including
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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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. 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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, Bayesian and maximum likelihood approaches, spatial statistics and random forests or other machine-learning approaches and be quick to learn new techniques. Enjoyment of analysis of large and spatially
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two issues: (1) It aims to develop new technical instruments to diagnose the quality of machine learning (ML) decisions; identify its failures; and identify root causes of such failures; and (2) it aims
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quantitative and digital methods, such as descriptive/inferential statistics, data modelling, machine learning (ML), experimental prototyping and technology ideation. A significant degree of autonomy is required
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for manufacturing operations. Process control: process modelling, control, and optimization, with applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in
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(particularly under extreme conditions), and/or the use of machine learning for solid mechanics/stress analysis problems are encouraged to apply. The job description presented here is deliberately broad due