The Tissue Image Analytics (TIA) Centre is a university research centre based in the Computer Science department. It was established in Jan 2021 to tackle some of the major challenges in systematic data-driven mining of an increasing deluge of cancer image data as well as the associated clinical and genomic data. Our ultimate aim is to develop cutting-edge AI technologies to assist pathologists in diagnosing cancer more efficiently and empower the oncologists with reliable information to select optimal personalised treatment for cancer patients.
Current research in the TIA centre is focussed on the application of image analysis and machine learning algorithms in order to further our understanding of the biology and entangled histological patterns of complex diseases such as cancer. We strive to be a hub of research excellence in the area of computational pathology and associated research areas, in order to tackle grand challenges in cancer diagnostics and treatment with a multi-disciplinary team of researchers and to make positive impact on the lives of cancer patients. Our research thrives on a growing network of collaborations with the academia, NHS hospitals and industry.
We are always looking for highly self-motivated PhD applicants with strong academic background in computing, electrical engineering, mathematics, or statistics preferably at the MS level, competency in modern machine learning and analytical methods and fluent programming skills in Python and R. Since most of the research in the TIA Centre involves working with clinicians and biologists as well as other members of the Centre, applicants should be comfortable working in a team, possess good communication skills, and be open to learning about the required knowledge in the related disciplines (medicine or biology).
- Establish a hub of research excellence in the emerging area of computational pathology, building on a growing network of academia, NHS hospitals and industry
- Contribute to making societal impact on the provision of improved healthcare for patients
- Tackle grand challenges with a multi-disciplinary team of researchers that could not be addressed by researchers working in a single discipline alone