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short courses in the core subjects of this PhD programme including process intensification and green chemistry. This project is part of the Process Industries: Net Zero (PINZ) Centre for Doctoral training
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, University of London in August 2024. As a PhD candidate, you'll become an integral part of the School of Science and Technology (proud member of the Alan Turing University Network) and be supervised by leading
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provider: EPSRC DTP 25/26 Fully Funded Studentship Subject area: Algae biotechnology/computational biology/microbial community networks/ecological theory/evolutionary mechanisms/biomass productivity
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each year. Modern applications—from power grids to vehicle platoons—depend on large networks of autonomous subsystems. Without a solid theoretical underpinning, ensuring both collective objectives
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• Comprehensive skills training across the entire barley supply and value chain – bespoke to meet industry needs • Regular networking opportunities across a large and diverse consortium comprising 22 companies, 42
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services would be to keep children close to their usual home (where safe to do so) and maintain their family and community networks often this is not possible and high cost out of area placements
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-informed data analytics tools for the predictive maintenance (PdM) strategy applications to high-value critical assets. Among others, the recently developed Physics-informed Neural Network (PINN) technique
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proposal, as it is already defined by us. You are encouraged to contact the lead supervisor of the project, Dr Silvia Bergamini, for any informal enquiries. Interviews will take place remotely shortly after
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. Our approach enhances T2 (Interconnected QC systems) through verification methods for connected networks, supports T1 (Integrated quantum demonstrators) via hardware-agnostic metrics, and enables T3
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of Science and Technology (proud member of the Alan Turing University Network) and be supervised by leading experts in machine learning for healthcare. You will also be affiliated to the School of Health