Identification of optimal treatment path using artificial intelligence in acute leukaemia

About the Project

Childhood Acute lymphoblastic leukemia (ALL) is one of the most common childhood cancers and a major success story of modern medicine, with survival rates now exceeding 85%. However, treatment remains long and intensive, often associated with significant short- and long-term toxicities. Some children relapse despite aggressive therapy, while others may receive more treatment than necessary. There is an urgent need to personalise therapy—maximising cure while minimising harm.

This interdisciplinary PhD project will harness artificial intelligence (AI) to identify the optimal treatment pathway and dosing strategy for individual children with ALL. By integrating genomic, clinical and treatment-response data, the project aims to move beyond current risk stratification models towards dynamic, data-driven treatment optimisation.

You will work with large, clinically annotated datasets incorporating:

• Genomic alterations
• Treatment response and measurable residual disease (MRD) data
• Demographic and biological variables

Using advanced machine learning and deep learning approaches, you will develop predictive models to:

• Improve relapse risk prediction
• Model treatment response trajectories
• Identify patient-specific dosing strategies
• Simulate alternative therapeutic pathways

The project combines methodological innovation with real clinical impact. Models developed during the PhD will be designed with interpretability and translational potential in mind, ensuring relevance to clinical decision-making.

Training and Research Environment

This studentship offers comprehensive interdisciplinary training at the interface of AI, genomics and paediatric oncology. You will gain expertise in:

• Machine learning and deep learning methods
• Survival analysis and treatment optimisation modelling
• Cancer genomics and clonal evolution
• Reproducible and ethical data science

You will be embedded within a collaborative research environment, working closely with computational scientists, genomic researchers and clinicians. Access to high-performance computing infrastructure and real-world clinical datasets will enable rigorous and impactful research.

Who Should Apply?

We welcome applicants with backgrounds in computer science, data science, mathematics, statistics, bioinformatics, physics, or biomedical sciences with strong quantitative skills. Prior experience in both AI and cancer biology is not essential enthusiasm for interdisciplinary research and improving outcomes for children with cancer is key.

This project offers the opportunity to apply cutting-edge AI methods to a pressing clinical challenge, developing skills highly valued in academia, biotech and healthcare innovation while contributing to meaningful improvements in paediatric cancer care.

Funding

Students who have, or are expecting to attain, at least an upper second-class honours degree (or equivalent) in a relevant subject, are invited to apply. Funding is available for Home (UK) students to cover tuition fees, a tax-free stipend at the UKRI rate (indicative amount in year 1 in 2026-27, £21,805) and research costs, for four years. Applicants normally required to cover International fees will have to cover the difference between the Home and the International tuition fee rates. There is no additional funding available to cover NHS Immigration Health Surcharge (IHS) costs, visa costs, flights etc.

Funding for this studentship is awarded on a competitive basis and is not guaranteed; availability will depend on the outcome of the selection process and subject to final approval by the University.

HOW TO APPLY

Please complete the following application form – Google Form

Applicants can only apply for 1 project; any additional applications will not be accepted.

Applicants should send the following documents to FMSstudentships@newcastle.ac.uk:

• a CV (including contact details of at least two academic (or other relevant) referees).
• a Cover letter – stating your project choice, as well as including additional information you feel is pertinent to your application.
• copies of your relevant undergraduate degree transcripts and certificates.
• a copy of your IELTS or TOEFL English language certificate (where required)
• a copy of your passport (photo page).

A GUIDE TO THE FORMAT REQUIRED FOR THE APPLICATION DOCUMENTS IS AVAILABLE

Please submit your documents in the following format only:

• each document should be submitted as a separate attachment and should be named as follows: candidate surname, candidate name – document type. For example: Jones, Jamie – CV; Jones, Jamie – cover letter.
• Please submit .pdf documents where possible for your CV, cover letter, transcripts and certificates. Do not submit photos of certificates.
• Do not combine documents into one pdf. You may zip separate documents into a zip file to send via email if required.
• When emailing your application, please use the email subject header: FMS PhD Application 2026

Applications not meeting these criteria may be rejected.

Informal enquiries may be made to the lead supervisor of the project you are interested in.

The deadline for all applications is 12 noon BST (UK time) on Wednesday 20th May 2026.