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Field
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candidate will work on modelling, analysis, and optimization of circular agri-food supply chains in the UK with a broader emphasis on identifying data-driven and practical solutions to minimize food waste and
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(PI), Co-PI and the research team members to ensure all project deliverables are met. Undertake these responsibilities in the project: the project: i. Perform fermentation and optimization of GRAS
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optimization; bioprospecting for novel microbial hosts, enzymes, and metabolites to support sustainable biomanufacturing. (Bio)pharmaceuticals: Processing science and technology for bio- and chemical
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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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candidate will work on modelling, analysis, and optimization of circular agri-food supply chains in the UK with a broader emphasis on identifying data-driven and practical solutions to minimize food waste and
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yeast and fungi for food ingredient and biochemical production. The specific goals are: Metabolic engineering of Yarrowia and Candida yeasts for organic acid production Design and optimization
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design, enabling timely analysis during design cycles. These components significantly impact engine/airframe integration and fuel burn, making efficient design methods essential for optimizing overall