This project aims to address the critical field of cancer research, a leading cause of death globally. The significance of this study lies in its potential to unravel the complex biological mechanisms underlying cancer through the identification of biomarkers, which are crucial for early detection, effective treatment, and understanding the progression of cancer. The multi-omics data to be studied will encompass genomics, proteomics, metabolomics, etc, and will provide a comprehensive view of the molecular landscape of cancer, offering insights that single-omics studies cannot. The focus on interpretable machine learning is key to this research. By employing advanced techniques like generative modelling and graph neural networks, the project aims to not only identify biomarkers with high accuracy but also provide insights into their biological relevance and interactions. Interpretable machine learning models offer transparency in their decision-making process, which is essential for clinical acceptance and understanding of complex cancer biology. The expected output of this project includes the development of sophisticated, interpretable machine learning models capable of analyzing complex multi-omics data. These models will potentially identify novel biomarkers, offering valuable contributions to cancer diagnosis, prognosis, and treatment strategies. The research also aims to publish findings in high-impact journals, advancing the field of computational biology and providing a new lens through which to view and combat cancer. Eligibility Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline. Funding At Manchester we offer a range of scholarships, studentships and awards at university, faculty and department level, to support both UK and overseas postgraduate researchers applying for competition and self-funded projects. For more information, visit our funding page or search our funding database for specific scholarships, studentships and awards you may be eligible for. Before you apply We strongly recommend that you contact the supervisor(s) for this project before you apply. How to apply Apply online through our website: https://uom.link/pgr-apply-fap When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees. Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered. After you have applied you will be asked to upload the following supporting documents: Final Transcript and certificates of all awarded university level qualificationsInterim Transcript of any university level qualifications in progressCVContact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)English Language certificate (if applicable)If you have any questions about making an application, please contact our admissions team by emailing [email protected]. EDI Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status. We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).