[Stipend at UKRI rate.] This PhD studentship at University College London (UCL) offers an exciting opportunity to investigate the structural diversity and antimicrobial potential of ultra-long complementarity-determining region (UL-CDR) loops in antibodies. The project is jointly supervised by Professor Christine Orengo (The Orengo Group), Dr. Gorka Lasso (The Lasso Lab), and Professor Kartik Chandran (Albert Einstein College of Medicine), providing a multidisciplinary and collaborative research environment. Antibodies, especially those with bovine UL-CDRs, represent a unique class of therapeutics with applications in infectious disease, cancer, and immunotherapy. These UL-CDRs possess a distinctive architecture, including a β-ribbon stalk and a disulfide-rich knob mini-domain, which confer high stability and independent folding. Despite their promise, the structural and functional diversity of UL-CDRs is not well understood. This project aims to characterise the sequence, structure, and function of UL-CDRs using deep learning and structural bioinformatics, with the ultimate goal of identifying novel antimicrobial peptide candidates. The research will involve deep learning-based modelling and clustering of a large dataset of UL-CDR sequences, analysis of sequence–structure relationships, and identification of structural similarities between UL-CDRs and proteins from various organisms. The student will also predict potential antimicrobial and therapeutic functions of UL-CDRs and collaborate with experimental virologists to validate computational predictions. Through this project, the student will gain expertise in structural bioinformatics, protein modelling, machine learning for biological data, large-scale sequence and structure analysis, functional annotation, and evolutionary analysis. The training environment includes access to GPU-enabled HPC clusters, high-end workstations, and regular group meetings, fostering interdisciplinary collaboration and mentorship. The project is part of the Centre for Doctoral Training in Engineering Solutions for Antimicrobial Resistance, offering a supportive and innovative setting for research and professional development. Applicants should have a background in bioinformatics, computational biology, structural biology, or related fields. Experience with protein modelling, machine learning, or sequence analysis is advantageous, and an interest in antimicrobial research and therapeutic development is desirable. The studentship provides a stipend at the UKRI rate, and applications should be submitted by 12th January 2026. For further details and to apply, visit the Centre for Doctoral Training in Engineering Solutions for Antimicrobial Resistance at UCL.