Research Fellow - Department of Cancer and Genomic Sciences - 106048 - Grade 7

University of Birmingham; Birmingham; On-site; GBP 36,000 - 46,000; Full time

Job summary

A leading UK research university is seeking a researcher for a project aimed at improving brain tumour diagnosis through AI. This role involves quantitative analysis of MRI data and developing machine learning algorithms. Ideal candidates should have a strong background in MRI analysis and paediatric oncology. The position offers a full-time fixed-term contract with a competitive salary range and opportunities for progression.

Qualifications

• Experience in quantitative analysis of MRI/Spectroscopy.
• Knowledge of paediatric brain tumour diagnosis and tumour biology.
• Experience developing machine learning/AI based classifiers.

Responsibilities

• Lead a project to develop AI tools for tumour diagnosis.
• Conduct analysis of MR Diffusion Weighted Imaging.
• Contribute to research funding bids.

Skills

Experience in quantitative analysis of MRI/Spectroscopy

Knowledge of paediatric brain tumour diagnosis

Machine learning/AI development

Proficiency in R and Python

Education

First degree in relevant field

Higher degree or pending examination in related research area

Job description

Position Details

Department of Cancer and Genomic Sciences, School of Medical Sciences, College of Medicine and Health

Location: University of Birmingham, Edgbaston, Birmingham UK

Full time starting salary is normally in the range £36,130 to £45,413 with potential progression once in post to £48,149

Grade: 7

Full Time, Fixed Term contract up to September 2026

Closing date: 17th August 2025

Background

The Children’s Brain Tumour Research Team (CBTRT) (www.cbtrt.bham.ac.uk) at the University of Birmingham, set up by Professor Andrew Peet and now led by Dr John Apps brings together a multidisciplinary team of clinicians and researchers to improve the understanding, diagnosis, treatment and lives of children with brain tumours.

Brain tumours are the most common cause of cancer death in children. They are often first diagnosed on an MRI scan. These scans provide information on the tumour location and size, but for precise diagnosis doctors and patients usually rely on pieces of tumour removed by surgery. It can take up to four weeks to get a final diagnosis, making it hard for doctors to make treatment decisions and causing uncertainty and anxiety for patients and families. We need to improve and accelerate diagnosis for these tumours. This will give clinicians and patients more time and information to make the best decisions for each patient, potentially improving surgical treatment and reducing brain injuries

Modern MRI scans tell us about a tumour’s biology. Through advanced computing (radiomics), it is possible to extract much more information from MRI images than is visible by eye. Combined with artificial intelligence (AI) approaches we can help diagnose tumours when first identified on a scan. For 20 years through the Imaging of Tumours Study, we have collected a world-leading database of MRI images of childhood tumours and have developed AI approaches to diagnose different types of tumour. To be useful for patients, this needs to be delivered in hospitals in real time. Our software tool (MIROR) enables clinicians to analyse new cases and compare with previous cases of known tumour types and offers an AI prediction of tumour type and a confidence score.

The researcher will lead a project funded by the Grace Kelly Cancer Childhood Trust to test the performance of this tool, further develop and improve the tool, increase the number and range of tumours the programme uses, improving the AI and MRI algorithms, and linking them with information from biological studies on tumour tissue.
This project harnesses AI to improve diagnosis and clinical decision-making leading to improved surgery, reduced side-effects, better and quicker treatment decisions and reduced uncertainty for patients and families.

The Children’s Brain Tumour Research Team is part of the Department of Cancer and Genomic Sciences at the University of Birmingham.

Role Summary

• Deliverthe research aims outlined within the Grace Kelly Childhood Cancer Trust grant entitled, “Advancing Non-Invasive Brain Tumour Diagnosis through AI driven Clinical Decision Support”.
• This includes, but is not limited to
  • Extraction of quantified features from Diffusion Weighted MRI and MR Spectroscopy, Use, further development, and testing of machine learning/AI algorithms
  • Integration of radiomic and biological datasets
  • Working closely with Medical Physics colleagues on reviewing recommendations for detection of specific metabolites
  • Support the curation of research data and code in line with a quality management framework

• Contribute to generating funding through contribution to writing further grant/funding bids to support ongoing research in the field

• Operate within area of specialism, analyse and interpret and present research findings and results

• Contribute to licensing or spin out deals with demonstrated commercial success (such as revenues, asset or company sales, IP generated) and/or public understanding of the discipline or similar

Main Duties

The responsibilities may include some but not all of the responsibilities outlined below.

• Deliver the research aims outlined within the Grace Kelly Childhood Cancer Trust grant entitled, “Advancing Non-Invasive Brain Tumour Diagnosis through AI driven Clinical Decision Support”, through analysis of MR Diffusion Weighted Imaging and MR Spectroscopy and leading other members of the wider team
• Specific aims of the project
  • To prospectively evaluate non-invasive pre-surgical radiomic diagnostic classifiers on brain tumours registered to the Imaging of Tumours Study
  • Improve classifiers through addition of additional cases to the repository and further optimisation of classification algorithms
  • Correlate/Integrate In Vivo and Ex Vivo metabolite analysis to understand the key metabolic pathways in different tumour types and subtypes
  • Identify and harmonise MRI and MRS acquisition protocols for detection and quantification of specific metabolites relevant for specific tumour types/subtypes (led by Medical Physics team)
• Contribute to writing bids for research funding
• Analyse and interpret data
• Apply knowledge in a way which develops new intellectual understanding
• Disseminate research findings for publication, research seminars etc
• Supervise students on research related work and provide guidance to MSc and PhD students where appropriate to the discipline
• Contribute to developing new models, techniques and methods
• Undertake management/administration arising from research
• Contribute to Departmental/School research-related activities and research-related administration
• Contribute to enterprise, business development and/or public engagement activities of manifest benefit to the College and the University, often under supervision of a project leader
• Collect research data; this may be through a variety of research methods, such as scientific experimentation, literature reviews, and research interviews
• Present research outputs, including drafting academic publications or parts thereof, for example at seminars and as posters
• Provide guidance, as required, to support staff and any students who may be assisting with the research
• Deal with problems that may affect the achievement of research objectives and deadlines
• Promotes equality and values diversity acting as a role model and fostering an inclusive working culture.

Person Specification

• First degree in area of specialism and a higher degree (or will have submitted pending examination at the time of starting the post), relevant to research area or equivalent qualifications
  • Experience in quantitative analysis of Magnetic Resonance Imaging/Spectroscopy
  • Ideally, experience and knowledge of paediatric brain tumour diagnosis and tumour biology
  • Experience of using and developing Machine learning/AI based classifiers
  • Proficiency in coding using R and Python and other similar languages
• High level analytical capability
• Ability to communicate complex information clearly
• Fluency in relevant models, techniques or methods and ability to contribute to developing new ones
• Ability to assess resource requirements and use resources effectively
• Understanding of and ability to contribute to broader management/administration processes
• Contribute to the planning and organising of the research programme and/or specific research project
• Co-ordinate own work with others to avoid conflict or duplication of effort
• Knowledge of the protected characteristics of the Equality Act 2010, and how to actively ensure in day to day activity in own area that those with protected characteristics are treated equally and fairly
• Good understanding of research data management, consent and confidentiality and quality management frameworks
• Be able to work collaboratively with clinical, research and administrative staff.

Further particulars can be foundhere

Informal enquiries to John Apps, email: [e-mail]

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