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the synergistic effects of monoclonal antibodies and colistin. Building on extensive preliminary data, the project aims to uncover the mechanisms behind this synergy using advanced biophysical and molecular
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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
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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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-scale metagenomic assembly and genome recovery • Comparative genomics and molecular evolution • Machine-learning-based protein prediction • Data integration, bioinformatics and phylogenetics • Scientific
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, host stress, and yield bottlenecks Data-driven design iteration: Integrate empirical data to refine AI-generated plasmid designs Impact and Outlook: This project will deliver scalable, deployable
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learning and machine learning for biological data Sequence and structure analysis of large-scale datasets Functional annotation and evolutionary analysis Collaborative research with experimental virology
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culture and in vitro models of mucosal immunity Data analysis using Python and digital image processing These skills are highly transferable to careers in biomedical research, pharmaceutical development
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and fluorescence imaging • Microfabrication and surface engineering • Quantitative microscopy and data analysis • Interdisciplinary collaboration across microbiology, engineering, and biophysics
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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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Microfluidics and lateral flow assay engineering Translational diagnostics and AMR-focused assay development Digital image analysis and Python-based data processing The project includes opportunities