17 computational-physics-"https:"-"https:"-"https:"-"Shenzhen-Technology-University" PhD positions 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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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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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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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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Engineering, Physical Sciences, and Mathematical Sciences. Why chose UCL? UCL has a history of academic excellence and is consistently ranked among the world's top universities, with many of our faculties
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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