This fully funded PhD opportunity at Queen's University Belfast focuses on AI-enabled vaccine discovery for antimicrobial-resistant (AMR) pathogens, integrating machine learning, experimental immunology, and bioscience within a One Health framework. The project is part of the NILAB (NI Landscape partnership in AI for Bioscience) and is supported by a BBSRC studentship. The research aims to develop an AI-driven, experimentally grounded antigen-discovery pipeline to identify protective vaccine targets against priority AMR bacteria, with a particular focus on Streptococcus pathogens affecting both humans and livestock. Students will work at the intersection of computational and experimental science, developing interpretable machine-learning models to identify high-value vaccine targets and validating them in the lab. The project leverages a unique dataset from the Ingram lab, featuring laboratory-measured immune responses and protective outcomes from infection models across key AMR pathogens. This robust foundation enables the development of AI models grounded in real biological data, not just computational predictions. The core research question investigates whether AI models trained on experimentally validated antigen datasets can learn transferable 'protection signatures' to accurately predict protective antigens against emerging One-Health Streptococcus pathogens (GBS, S. suis) across human and livestock hosts. The project includes close academic–industry collaboration, with opportunities for placements in biotech companies and exposure to commercialisation and regulatory science. Applicants should have a strong background in computer science, bioinformatics, mathematics, statistics, or a related quantitative discipline. Experience with Python, machine learning, structural bioinformatics, or immunology is advantageous but not essential, as full interdisciplinary training will be provided. The training environment is designed to support students eager to work at the intersection of AI, biology, and translational research, with co-supervision from the Ingram lab (infection immunology and in vivo models) and the Wang lab (AI for bioscience). Funding is available for both Home and International applicants, though international awards are limited and allocated competitively. The position is fully funded, covering tuition and providing a stipend. The application deadline is 16 February 2026, with a start date of 28 September 2026. To apply, submit your CV and a covering letter via the Queen's University Belfast application portal, referencing project number SMED-2261-1029. For more information, visit the project and NILAB programme links or contact [email protected].