EPSRC Centre for Doctoral Training in Health Data Science
A four-year, full-time programme in big data for medicine at Oxford, leading to a D.Phil (Ph.D) in health data science: a year of training in statistics, machine learning, health data, ethics and responsibility, followed by a three-year project with a leading research group; placement opportunities with partner organisations, including technology and pharmaceutical companies, research organisations, and healthcare providers; fully-funded studentships are available for UK/EEA students who meet residency requirements.
|Jim Davies is the Director of the Health Data Science CDT. He is the Professor of Software Engineering within the Department of Computer Science, and a group leader within the BDI.|
|Thomas Nichols is a co-Director of the CDT. He is the Professor of Neuroimaging Statistics within the Department of Statistics, and a group leader within the BDI.|
|Nina Hallowell is co-Director of the CDT, and is an Associate Professor of Ethics and Population Health in the Nuffield Department of Population Health, and leads the Big Data Ethics Group within the Ethox Centre|
|Jens Rittscher is co-Director of the CDT focusing partner relations. He is a Professor of Engineering Science in the Department of Engineering Science and the Nuffield Department of Medicine, and a group leader within the BDI.|
Teaching, mentoring, and research supervision is provided by a wide range of leading specialists from the Departments of Computer Science, Statistics, and Engineering Science, the Nuffield Department of Population Health, and the Nuffield Department of Medicine.
Data science and artificial intelligence will transform the way in which we live and work, and some of the greatest and most urgent opportunities for transformation lie in the field of human health. We are seeking bright, driven individuals who want to use data science to build tools to advance human health, whether that is predicting and diagnosing disease, determining the effectiveness of existing treatments, or improving the quality and affordability of care.
With support from the EPSRC, the University offers a four-year programme in data science for health research and healthcare delivery, leading to a D.Phil in Health Data Science. The training is provided by five university departments - Computer Science, Engineering Science, Statistics, Medicine, and Population Health - working together to provide a coordinated programme of collaborative learning, practical experience, and research supervision.
Students on the Programme are based initially in the Big Data Institute, which acts as a hub for multi-disciplinary research at the heart of the University's medical campus, before undertaking a three-year project with a research group from one of the five contributing departments. In many cases, this group will also be based within the Institute. Students will have the opportunity to contribute to teaching, and will undertake a range of cohort-based, cross-departmental activities. Many research projects will be undertaken in collaboration with a partner organisation: a technology or pharmaceutical company, a healthcare provider, or a research organisation.
The first year starts with two terms of intensive training in health data science, each beginning with an induction or orientation week, and ending with a collaborative data challenge, in which students will work with partner organisations to solve real-world problems.
Term 1 orientation (team science, big data ethics and governance), computational statistics, machine learning, data engineering, information security; big data challenge.
Term 2 orientation (health research, health data ethics and governance), electronic health records, medical imaging, genomics, sensor data, health research methodologies; health data challenge.
This training will be delivered by specialists from the Departments of Computer Science, Engineering Science, Statistics, Medicine, and Population Health. In this first year, academic supervision will be provided by the Centre Directors, all of whom are based in the Big Data Institute.
In the third, summer term, students will undertake two short placements and develop a research proposal, in collaboration with a potential research supervisor.
Students will work with potential research supervisors and partner organisations to develop research proposals in the development and application of data science in the context of health research and healthcare delivery. The research will involve the development of new methodologies, new languages, or new algorithms for the management, representation, and analysis of large amounts of complex, health-related data. Students can develop proposals and undertake projects in the following areas:
Medical Imaging analysis of large imaging datasets; brain imaging measurements for clinical dementia assessment; radiological and clinical predictions from spinal imaging; mapping dynamic brain activity; facial phenotyping for rare diseases; early life development and outcomes from photographic data.
Sensors and Wearables longitudinal monitoring of symptoms for diseases such as Parkinson's; relating activity data to disease onset; using accelerometer and camera data in large cohort studies; extracting clinically-meaningful lifestyle behaviours from sensor and wearable data.
Cancer Informatics modelling, comparing, and optimising cancer treatment pathways; re-using routinely-collected and population-level data in cancer research; technologies for early detection and monitoring of progression; identifying new disease subtypes based upon clinical and laboratory data, as well as imaging and sequencing technology.
Health Data Security enhancing the privacy and security of medical devices using augmented reality systems; physiological signals for user authentication; blockchain or distributed ledger technologies for the security and integrity of healthcare and health research data; blockchain technologies for health data provenance and interpretation.
Genomic Medicine the incorporation of data derived from whole genome sequencing in healthcare records; the ethical and responsible management of genomic data; the healthcare economics of genomic medicine; the analysis of genomic data in the context of longitudinal health records.
Electronic Health Records structure and interpretation of health records; supporting clinical and translational research using routinely-collected data; knowledge management and decision support; explaining artificial intelligence in healthcare; early warning of patient deterioration based upon time-series data in a hospital setting.
For the duration of their project, each student will have two academic supervisors: at least one will have significant expertise in a core data science discipline: computer science, statistics, or engineering; the other may be a research leader in medicine or population health. Depending upon the composition of the supervisory team, students may spend some or all of their project working in a different department. If the project involves a partner organisation, students may also undertake placements at or short visits to another site.