AI-Driven Multi-Omics Analysis for Cancer Antigen Discovery and Precision Immunotherapy

This PhD project combines Artificial Intelligence, Bioinformatics, and Computational Cancer Biology to investigate cancer-specific molecular alterations and identify novel targets for precision immunotherapy. The successful candidate will develop computational approaches to integrate and analyse large-scale multi-omics datasets, including genomics, transcriptomics, proteomics, and immunopeptidomics, leveraging both publicly available biological datasets and experimental data generated through ongoing cancer research projects. The project will focus on: Developing computational workflows using Python, R, and advanced bioinformatics tools. Applying machine learning, AI, and statistical modelling to complex biological datasets. Integrating publicly available multi-omics resources with in-house experimental datasets to identify cancer-specific molecular signatures. Investigating relationships between gene expression, protein abundance, antigen presentation, and tumour biology. Developing predictive approaches to identify and prioritise potential immunotherapy targets. Ideal Candidate We encourage applications from students with backgrounds in Computer Science, Bioinformatics, Data Science, Computational Biology, Statistics, or related disciplines. Strong programming skills and an interest in AI, machine learning, and biological data integration are highly desirable. Previous experience in cancer biology is not essential. The project offers an opportunity to develop advanced computational skills while contributing to translational cancer research and precision immunotherapy.