skip to content

Graduate Admissions


Applications are invited for the Voellm-Hruska PhD studentship starting in either April or October 2020 in the research group led by Professor David Wales on a project that aims to advance machine learning methods as predictive tools with applications to clinical diagnosis. This research involves a collaboration with Dr Ari Ercole, a clinician and physicist at Cambridge University Hospitals NHS Foundation Trust / University of Cambridge division of anaesthesia with expertise in intensive care and big data analysis for clinical data.

Machine learning methods show great promise as predictive tools in clinical diagnosis. In particular, the late identification of deteriorating patients results in treatment delays that contribute to morbidity and mortality. Current early-warning and predictive scores are implemented largely manually and are relatively crude, labour intensive and take into account only a fraction of the patient data now available in digital form. Big data from routinely collected healthcare records is a resource with enormous potential. However, making inferences from such high dimensional, heterogeneous, irregularly sampled data to help the clinician with timely and more accurate decision making is challenging. This project aims to advance the state-of-the-art using cutting-edge techniques developed in the context of potential energy landscapes. The overall theme is "predicting stable and deteriorating patients", which could include earlier diagnosis of sepsis and analysis of readmissions to the intensive care unit.

Further information about Professor Wales' research can be found at

Applicants should have (or expect to obtain) the equivalent of a UK first class or upper second-class honours degree (and preferably a Masters) in chemistry, physics, mathematics, computer science or materials science. This is a highly interdisciplinary project; interest in machine learning is essential, and previous experience of programming in the context of physical science or mathematics would be advantageous.

The studentship is funded by Downing College; the successful applicant is required to become a member of Downing College.

Applications should include a cover letter, CV, detailed academic transcripts, and the contact details for at least two academic referees, and should be sent by email to Professor David Wales (via, to whom any informal enquiries can be addressed.

If you wish to be considered for any other available studentships in the Chemistry Department, you must also apply online via the University Applicant Portal by December 3rd 2019 (further information at Please note that there is an application fee.

Information about research in the Department of Chemistry is given at:

Please quote reference MA21010 on your application and in any correspondence about this vacancy.

Key Information

Department of Chemistry

Reference: MA21010

Dates and deadlines:

Tuesday, 8 October, 2019
Closing Date
Tuesday, 3 December, 2019