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About the Project A fully funded PhD scholarship is available at UCL Electronic & Electrical Engineering (4 years, home tuition fees covered, stipend provided). Exceptional international candidates
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: Machine Learning Molecular Dynamics. The project involves the development and application of machine learning methods that enable a major boost of the time and length scales accessible to ab-initio/first
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Supervisors: Prof Manish Tiwari Prof Shervanthi Homer-Vanniasinkam Clinical Partner: The Royal National Orthopaedic Hospital (RNOH) Collaborator: Dr. Priya Mandal – UCL Mechanical Engineering
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multidisciplinary environment and access to cutting-edge facilities at UCL and the Royal Institution. The supervisory team offers strong mentorship and professional development opportunities, including support for
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promising targets for antiviral drug development. While the COVID-19 pandemic highlighted the threat of RNA viruses, large DNA viruses such as African Swine Fever Virus (ASFV) remain underexplored despite
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, more effective medical devices and reduce reliance on antibiotics, contributing to global AMR mitigation efforts. Training and Student Development: The student will gain interdisciplinary training in
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, reduce the need for systemic antibiotics, and mitigate the development of AMR in oral pathogens. The approach may also be extended to other biomedical applications involving biofilm-associated infections
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treatment outcomes in vulnerable populations. Training and Student Development: The student will receive interdisciplinary training in: Peptide synthesis and structural analysis Advanced mass spectrometry
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enzymes. Mapping bacterial defence systems to infer predictive features of co-evolutionary dynamics. Impact and Outlook This project will: • Advance understanding of microbial co-evolution. • Deliver a
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techniques. The findings will lay the groundwork for clinical application and contribute to the development of targeted therapies for resistant bacterial infections. Approach and Methods Atomic force