17 computational-physics "https:" "https:" "https:" "https:" "Ulster University" PhD scholarships at UCL
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refer to the following website for eligibility criteria: https://www.ucl.ac.uk/prospective-students/graduate/research-degrees/physics-and-astronomy-mphil-phd . The studentship will cover university fees
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contact them directly for informal discussion of potential project ideas. The secondary supervisor will be allocated by UCL as part of the formal admission process. Application process If you are interested
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across clinical and physical sciences Research Environment Hoogenboom Lab: Pioneers in AFM imaging of bacteria, supported by BBSRC and Wellcome. Brown Lab: Leader in pneumonia pathogenesis, equipped
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pharmaceutical R&D Application of AI in process development and materials discovery Antimicrobial testing and biological evaluation Workflow integration and data management for self-driven laboratories Project
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at scale. Training and Student Development: The student will gain interdisciplinary training in: Advanced molecular cloning, CRISPR-Cas systems, and genome engineering Bioreactor operation and process
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PhD Studentship: Nanopore Technology for Rapid and Accurate Measurement of Antibiotic Concentrations
biotechnology, large-scale DNA sequencing, and directed evolution. Together, the labs provide a dynamic and supportive environment for interdisciplinary research at the interface of physics, biology, and
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that suppress resistance gene transfer. Combining biophysics, microbiology, and materials science, the project will generate insights into how physical environments can be harnessed to control AMR. Approach and
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cultivation and molecular cloning Quantitative data analysis and process optimisation Synthetic and molecular biology techniques Research design, interdisciplinary collaboration, and scientific communication
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with experimental virologists to validate computational predictions Impact and Outlook: This project will uncover the untapped structural and functional potential of bovine UL-CDRs, laying the groundwork
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Develop an active learning-driven platform for compound selection and optimisation Integrate robotic sample preparation, automated data acquisition, and computational analysis Advance five existing