Available
Project number:
2025_30
Start date:
October 2025
Project themes:
Main supervisor:
Lecturer in Forensic Chemistry
Co-supervisor:
Dr Vincenzo Abbate
Additional Information:
Design of a modular probabilistic expert system based on Object-Oriented Bayesian Networks to facilitate the interpretation and collation of toxicological findings
Despite significant steps forward in pharmacology, the interpretation of toxicological findings (for example, in case involving impairment or poisoning assessment) still remains a challenging procedure. Indeed, the biochemical and physiological effects of any drug detected in a biological sample effectively depend on numerous influential factors that are typically case-related and, therefore, very variable from case to case. Accurately weighing any finding against all these factors would require very accurate knowledge of the case circumstances, as well as extensive datasets. However, these are rarely available, even in the best-case scenarios. No robust and transparent interpretation framework currently exists to deal with the additional sources of uncertainty and variability that inevitably stem from any gap in the relevant circumstantial information and/or clinical data.
To deal with this challenge, the use of Bayesian inference and derived graphical modelling tools, such as Bayesian Networks (BN) and Object-Oriented Bayesian Networks (OOBN), have gained immense popularity in close fields, such as diagnostic medicine and forensic science. Although their use has also recently been suggested in toxicology, no extensive works have been carried out. Therefore, this project aims to progress the current state of the art in this direction and develop a modular interpretation system based on (OO)BN and statistical learning that will allow modelling all the variables affecting drug pharmacokinetics, as well as related metrics. This system will lead to a more robust and transparent way to interpret toxicological findings and, therefore, will also be the primer for the next generation of expert systems.
We are now accepting applications for 1 October 2025
How to apply
Candidates should possess or be expected to achieve a 1st or upper 2nd class degree in a relevant subject including the biosciences, computer science, mathematics, statistics, data science, chemistry, physics, and be enthusiastic about combining their expertise with other disciplines in the field of healthcare.
Important information for International Students:
It is the responsibility of the student to apply for their Student Visa. Please note that the EPSRC DRIVE-Health studentship does not cover the visa application fees or the Immigration Health Surcharge (IHS) required for access to the National Health Service. The IHS is mandatory for anyone entering the UK on a Student Visa and is currently £776 per year for each year of study. Further detail can be found under the International Students tab below.
Next Steps
- Applications submitted by the closing date of Thursday 6 February 2025 will be considered by the CDT. We will contact shortlisted applicants with information about this part of the recruitment process.
- Candidates will be invited to attend an interview. Interviews are projected to take place in April 2025.
- Project selection will be through a panel interview chaired by either Professor Richard Dobson and Professor Vasa Curcin (CDT Directors) followed by informal discussion with prospective supervisors.
- If you have any questions related to the specific project you are applying for, please contact the main supervisor of the project directly.
For any other questions about the recruitment process, please email us at
