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Duration of Studentship: Full-time – 3 Years Start Date: 1 October 2025 Application Deadline: 29 August 2025 Primary Supervisor: Professor Peter Munro (p.munro@ucl.ac.uk ) Secondary Supervisor
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Open to: UK applicants only Funding providers: EPSRC CASE Conversion with GSK Subject areas: Applied Mathematics, Mathematical Modelling, Mathematical Biology Project start dates: 1 October 2025 1
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. At the intersection of different sub-fields (applied mathematics, software engineering, and medicine), it is expected to refine the scope and goals of the project with the selected applicant, depending on technical
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will be secondary data analysis, linking reported incidents of violence with weather data to identify correlations between climate variables (e.g., temperature extremes, humidity) and violent crime
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analysis, and high-performance simulations? We offer a challenging PhD position in the Mathematics of Computational Science group to design and analyse novel structure-preserving methods for stochastic
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biology and secondary metabolism, aiming to turn these insights into commercial products such as bioherbicides, and biofungicides that benefit sustainable agriculture. A PhD position is now available within
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(applied mathematics, software engineering, and medicine), it is expected to refine the scope and goals of the project with the selected applicant, depending on technical skills and personal interests
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
DNA thermodynamic database, coarse-grained simulations of DNA motifs, and existing experimental data to establish an AI model that is able to guide the construction of desired secondary structures
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writing and presentation skills. At the start of the PhD, having obtained a Master’s degree in a relevant field, such as AI, mathematics, physics, (computational) neuroscience, etc.. Terms and conditions
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the spaces, but also behavioural changes, such as changing habits regarding ventilation or heating. The second part will employ secondary data analyses from existing cohort studies to explore the associations