Graduate Teaching Assistant (GTA) - Advancing the Optimisation of Simulation and Machine Learning Pipelines for Enhanced Performance Benchmarked in the Healthcare Domain

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:

1. Acquire data and expert-based evidence and optimise data augmentation to ensure optimal hospital patient pathways through pre-operative assessment, surgery, anaesthetic normal recovery, extended recovery, and ICU services: bookings, utilisation, pre-operative and on-the-day cancellations, emergencies, and, non-attendances.
2. Establish a clinical scoring system to predict patients’ post-operative anaesthetic recovery needs to three severity and associated care levels and respective predicted nurse-to-patient ratio required (complexity).
3. Develop and optimise a modelling pipeline including a decision support dashboard for optimal patient selection for surgery to ensure daily surgery caseload optimisation, post-operative care manageability, patient safety, and reduce waiting lists.

You will need to demonstrate you:

• Meet the academic requirements for a PhD offer from the University of Reading.
• Have a good (1st or 2.1) first degree in Computer Science, Statistics, Data Science, Mathematical Science, Meteorology, Physics or closely related subjects.
• Are able to effectively organise your time and prioritise tasks to balance PhD studies with GTA responsibilities.

How do I apply?

You must upload a combined CV and Proposal in pdf format (max size 1 MB) and complete the supporting statement.

Closing date: 4 April 2025

Interview: 24 April 2025

We look forward to hearing from you!

Contact details for advert

Contact Name Dr Ferran Espuny-Pujol

Contact Job Title Lecturer in Computer Science

Contact Email address f.espuny-pujol@reading.ac.uk

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