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The University of Reading seeks ambitious computer scientists for its Computer Science Graduate Teaching Assistant (GTA) Programme. This four-year, part-time position offers a funded PhD opportunity while providing a pathway to develop teaching skills and an enriching research career in optimizing healthcare processes.
Applications from job seekers who require sponsorship to work in the UK are welcome and will be considered alongside all other applications. However, non-UK candidates who do not already have permission to work in the UK should note that by reference to the applicable SOC code for this role, sponsorship will not be possible under the Skilled Worker Route. There is further information about this on the UK Visas and Immigration Website .
Part-time, fixed-term (4 year) role.
Closing date: 23:59 GMT 20 July 2025
Interview date: to be confirmed
We are pleased to announce a fantastic opportunity for ambitious computer scientists to join our Computer Science Graduate Teaching Assistant (GTA) Programme!
How does it work?
Candidates will study for a four year, full time funded PhD (3 quarters of your time) whilst working and receiving a salary to gain valuable teaching experience (1 quarter of your time). Candidates will receive a salary and stipend package that exceeds the standard UKRI stipend for a full-time PhD.
Home/RoI Students will have their PhD fees waived, International students will receive a fee waiver equivalent to the Home/RoI fee and will be expected to fund the difference between the International fee and the Home/RoI fee. There will be a package of support to enable you to develop a research career in this exciting field.
PhD Topic: Advancing the Optimisation of Simulation and Machine Learning Pipelines for enhanced performance benchmarked in the Healthcare Domain
The Royal Berkshire Hospital has 20 surgical theatres and only 3-4 beds for patients' overnight full recovery from anaesthesia. Thus to avoid exceeding the bed capacity a daily limit is imposed on those surgeries that are deemed likely to require overnight stay for post anaesthesia recovery. The needed post-operative care is predicted manually based on a pre-operative assessment for surgeries case selection to be scheduled for each day. This decision process is poorly recorded and needs improvement.
Aims and Objectives
In collaboration with the Health Innovation Partnership, a modelling pipeline will be devised to cope with the challenges of data augmentation and model optimisation to deliver a reliable prediction of patient needs into recovery services after surgery, improving the deployment of available resources, ensuring patient quality-of-care and reducing waiting lists.
This will be achieved through the following objectives: