Funding

Self-funded

Project code

CMP10251026

Start dates

October, February and April

Application deadline

Applications accepted all year round

Applications are invited for a self-funded, 3-year full-time or 6-year part time PhD project.

The PhD will be based in the School of Computing and will be supervised by Dr Ella Haig and Professor Sam Robson.

 

 

 

The work on this project will:

  • Apply advanced preprocessing techniques, including the use of pretrained foundation models, to prepare and integrate the clinical data with the genomic data from 16S rRNA amplicon sequencing and RNA sequencing from patient samples
  • Develop and evaluate predictive algorithms using state-of-the-art artificial intelligence and machine learning techniques, with a particular emphasis on transformer-based architectures 
  • Integrate explainable AI frameworks to elucidate model reasoning and identify the most influential features associated with prosthetic joint infection risk

Prosthetic joint infection represents one of the leading causes of failure in surgical interventions aimed at repairing or replacing damaged or diseased joints. Such infections not only compromise patient outcomes by necessitating revision procedures but also impose considerable socioeconomic burdens on both individual patients and the broader healthcare systems.

This PhD project aims to address this critical clinical challenge through the development of advanced predictive models that integrate heterogeneous data sources — notably, clinical parameters, pathogen-specific genomic data, and patient biomarkers. The overarching goal is to enable early and accurate prediction of prosthetic joint infection risk, thereby facilitating timely preventive strategies and reducing the reliance on corrective surgical interventions, as well as reducing the impact of antimicrobial resistance.

Leveraging recent breakthroughs in artificial intelligence, the project will employ transformer-based deep learning architectures — the class of models underpinning systems such as ChatGPT — alongside a suite of machine learning techniques to train, validate, and optimize predictive algorithms. The research will also incorporate explainable AI frameworks to interrogate the decision-making processes of the models, elucidating which features most significantly influence predictions. This interpretability will be vital for enhancing model transparency, fostering clinical trust, and informing personalized patient care.

This project offers an exceptional opportunity to engage in cutting-edge research at the intersection of artificial intelligence, clinical informatics, and genomics. By working with state-of-the-art transformer-based models and explainable AI frameworks, you will contribute to solving a pressing healthcare challenge while developing a robust skill set in predictive modelling, data integration, and algorithmic interpretability. Beyond technical expertise, you will develop transferable skills highly sought after in both academia and industry — including research design, interdisciplinary collaboration, critical analysis, and effective scientific communication. The project provides a dynamic environment to explore novel methodologies and produce impactful research with real-world translational potential, thus enhancing both academic and professional development.

 

 

Fees and funding

Visit the research subject area page for fees and funding information for this project.

Funding availability: Self-funded PhD students only. 

PhD full-time and part-time courses are eligible for the UK  (UK and EU students only).

 

Bench fees

Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.

Entry requirements

You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a master’s degree in computer science or a related area. In exceptional cases, we may consider equivalent professional experience and/or Qualifications.

English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

 

Strong foundation in programming (e.g., Python, R), knowledge of algorithms and data structures, understanding of molecular biology and genomics (or willingness to acquire it) and experience with statistical methods and machine learning.

 

 

How to apply

We’d encourage you to contact Dr Ella Haig (ella.haig@port.ac.uk) to discuss your interest before you apply, quoting the project code.

When you are ready to apply, please follow the 'Apply now' link on the Computing PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process. 

When applying please quote project code: CMP10251026