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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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of industry and healthcare partners, a rare opportunity to develop a highly sought-after interdisciplinary skill set that is in demand across both academia and industry. Training and Student Development
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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
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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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, 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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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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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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for engineering novel antimicrobial peptides. The findings could lead to the development of new therapeutic scaffolds with applications in infectious disease, biotechnology, and immunotherapy. The project also
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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