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Graduate Teaching Assistant (GTA) - Advancing the Optimisation of Simulation and Machine Learning Pi

University of Reading

Reading

On-site

GBP 8,000 - 16,000

Full time

30+ days ago

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Job summary

An exciting opportunity awaits ambitious computer scientists to join a unique Graduate Teaching Assistant Programme. This role offers a funded PhD while gaining invaluable teaching experience. Candidates will engage in groundbreaking research focused on optimizing simulation and machine learning pipelines within the healthcare sector. With comprehensive support for professional development, including access to MSc courses, this position fosters both academic and pedagogical growth. If you're passionate about research and education, this role is a perfect fit for you, paving the way for a rewarding career in academia and beyond.

Benefits

PhD fees waived for Home students
Training for teaching and pedagogical skills
Access to MSc courses
Bespoke training through Postgraduate College

Qualifications

  • Candidates must meet PhD academic requirements and have a relevant degree.
  • Effective time management and communication skills are essential.

Responsibilities

  • Study for a PhD while gaining teaching experience as a Graduate Teaching Assistant.
  • Develop a modelling pipeline to improve patient recovery predictions.

Skills

Organizational Skills
Communication Skills
Interpersonal Skills
Research Skills

Education

1st or 2.1 degree in Computer Science
Degree in Statistics, Data Science, or related fields

Job description

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.
  • are able to demonstrate scholarship in developing a publication record in your area of specialist expertise and conduct high-quality PhD research.
  • are able to communicate scientific concepts clearly and with enthusiasm and in a way that engages students.
  • have good interpersonal skills and be able to work as part of a team.

See candidate pack at the bottom of the page for further details.


Candidates will be provided with training to develop teaching and pedagogical skills; no prior experience of teaching is necessary. On the research side, our package of support includes access to MSc courses and bespoke training through our Postgraduate and Researcher College will help you in developing your professional skills as a researcher.


Working hours for the teaching portion will be variable during the academic year but will be no more than 20 hours per week. The terms of the offer of funding for the PhD and the offer of employment will rely upon the postholder being registered as a full-time doctoral student.


Successful candidates will be paid an annual salary (£8745) and stipend (£15585 per annum) over the 4-year period and will have PhD fees waived at the Home level (Please note that students liable for international fees will need to pay the difference between these and the home fee rate). Fees for 2025/26 (amount payable each year) can be found here.


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


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.


The University is committed to having a diverse and inclusive workforce, supports the gender equality Athena SWAN Charter and the Race Equality Charter, and champions LGBT+ equality. Applications for job-share, part-time and flexible working arrangements are welcomed and will be considered in line with business needs.

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