Postdoctoral Researcher in Machine Learning analysis of MRI

Postdoctoral Researcher in Machine Learning analysis of MRI
Band 7

Main area Division R&D-Postdoctoral Researcher in Machine Learning analysis of Magnetic Resonance Imaging (MRI) Grade Band 7 Contract Fixed term: 18 months (until 31/03/28) Hours

• Full time
• Flexible working

37.5 hours per week (Full Time or Part Time/ Flexible working hours may be considered) Job ref 180-RD-264062

Employer Cambridge University Hospitals NHS Foundation Trust Employer type NHS Site Addenbrookes Hospital-Division R&D Town Cambridge Salary £47,810 - £54,710 p.a. pro rata Salary period Yearly Closing 09/03/2026 23:59

Job overview

Postdoctoral Researcher in Machine Learning analysis of Magnetic Resonance Imaging (MRI)

Applications are invited for an enthusiastic and motivated Post-doctoral Researcher to join the Lysosomal Disorders Unit at Cambridge University Hospitals NHS Foundation Trust. The post (Band 7) is funded by an award from industry, for a term of two years. The successful applicant will work as part of a team of clinical and imaging specialists.

The ethically-approved award is to develop a machine-learning technique to simulate quantitative Dixon fat/water images from a database of traditional T1 and T2-weighted MR images of muscle. A large training set of Dixon fat/water images, paired with T1 and T2-weighted images of muscle and other structures, is available locally and from collaborators. The ultimate purpose is to develop a technique to monitor disease progression and response to treatment in genetic diseases of muscle, in which fat replacement is a hallmark of disease.

Such a technique may have wider application to other organs, tissues and disease states.

You will have a PhD in a relevant subject such as computer science or engineering, together with essential experience in advanced analysis of medical images, particularly machine-learning analysis of musculoskeletal MR. Experience in manual and machine learning segmentation of anatomic structures is highly desirable. A substantial publication record in the field is valued.

Main duties of the job

This is a collaborative translational research post focussing on the development of state-of-the‑art machine/deep learning‑based medical image analysis methods for MRI. The specific focus is to develop novel biomarkers of fat infiltration in muscle and other tissues for use in real‑world clinical settings.

Excellent organisational skills and the ability to work as part of a team, as well as independently, are also essential.

Please see the attached Applicant Information Pack for key duties and responsibilities.

Working for our organisation

Our Trust

Cambridge University Hospitals (CUH) NHS Foundation Trust comprises Addenbrooke’s Hospital and the Rosie Hospital in Cambridge. With over 13,000 staff and over 1100 beds the priorities of the Trust focus on a quality service which is all about people – patients, staff and partners. Recognised as providing ‘outstanding’ care to our patients and rated ‘Good’ overall by the Care Quality Commissioner, is testament to the skill and dedication of the people who work here. CUH’s values – Together - Safe, Kind, Excellent – are at the heart of patient care, defining the way all staff work and behave. The Trust provides accessible high‑quality healthcare for the local people of Cambridge, together with specialist services, dealing with rare or complex conditions for a regional, national and international population.

CUH is committed to promoting a diverse and inclusive community - a place where we can all be ourselves. We value our differences and fully advocate and support an inclusive working environment where every individual can fulfil their potential. We want to ensure our people are truly representative of all the communities that we serve. We welcome applications for all positions in the organisation irrespective of people’s age, disability, ethnicity, race, nationality, gender identity, sex, sexual orientation, religion or belief, marriage and civil partnership status, or pregnancy and maternity status or social economic background.

Detailed job description and main responsibilities

Please see the attached Applicant Information Pack (combined Job Description and Person Specification) for key duties and responsibilities.

Informal enquiries directed to Dr. Patrick Deegan () are encouraged.

This vacancy will close at midnight on 9th March 2026

Interviews are due to be held on 26th or 27th March 2026

Benefits to you

At Cambridge University Hospitals, we want to do all we can to support good working days. We offer development opportunities and a wide range of benefits, including on‑site leisure facilities, shopping concourse and day nurseries. Our good work programme currently includes providing reduced cost Stagecoach bus travel to and from Cambridge University Hospital site. Park and Ride bus journeys between Babraham Road and Trumpington sites are free, as is the route to and from Cambridge train station and our hospitals. We also subsidise the cost of parking on site for eligible staff.

On CUH campus, hot food is available 24/7 and at a reduced cost for colleagues. Recently we launched the first of our staff pod break spaces. Located in the Deakin Centre, we have a purpose‑created colleague‑only café, with free tea and coffee, a break space and private outside area for colleagues to rest, refuel and recharge. Just one of the ways we are working hard to support good working days at CUH.

CUH is committed to assisting employees in achieving a good work‑life balance irrespective of role or personal circumstances. Flexible arrangements may include, but are not limited to, part‑time working, job‑share, term‑time working and flexible start and finish times.

Please note if you would like to discuss the required hours of this role further, you should approach the contact given. In some cases, alternative working hours will be considered.

We welcome applications from the Armed Forces.

Person specification
Qualifications

• 
  
• PhD in relevant scientific or analytical discipline, especially related to machine learning, MRI, and image processing (eg Neuroscience, Computer Science, Physics, Bioinformatics, Engineering, Mathematics)
  

Experience

• 
  
• Experience of developing novel machine learning and deep learning methods in medical or biomedical image analysis
  
• Track record of relevant 1st author publications
  
• Expertise in analysis of MR images using deep learning techniques
  
• Experience with medical imaging data formats such as DICOM and NIfTI.
  
• Experience in statistical analysis using tools such as MATLAB, SPSS, and Python packages including NumPy, SciPy and pandas.
  
• Experience in ensuring reproducibility and documentation of analysis pipelines using version control systems such as GitHub.
  
• Experience in working with fat/water MR image processing.
  
• Experience in acquiring data with new MRI protocols/techniques for patient or volunteer studies
  
• Experience in manual segmentation of human anatomical structures (ideally muscle) using software tools such as ITK‑SNAP, 3D Slicer, or similar applications.
  
• Experience in developing, training and evaluating deep learning segmentation models such as U‑Net.
  
• Experience in managing and analysing large‑scale patient datasets, including integration of imaging data with clinical and demographic information
  
• Experience with PACS systems for handling, retrieving, and managing DICOM imaging data.
  
• Experience with cloud or high‑performance computing (HPC) environments for large‑scale image analysis.
  

Knowledge

• 
  
• Detailed understanding of machine learning principles and emerging AI trends, including cutting‑edge deep learning architectures and generative models
  
• Coding experience: Python and relevant deep learning frameworks such as PyTorch, Keras, and TensorFlow.
  
• Experience working in a Linux operating system environment.
  
• Knowledge of human anatomy, particularly the musculoskeletal system.
  
• An understanding of MRI physics, especially as relates to chemical shift imaging methods, such as Dixon, IDEAL techniques.
  
• An understanding of phase unwrapping mathematical or processing techniques.
  
• Knowledge of data governance, patient confidentiality, and ethical considerations in handling clinical imaging data.
  

Skills

• 
  
• Excellent verbal and written communication skills to Academic English standard
  
• Ability to communicate complex technical concepts clearly and effectively to non-technical colleagues
  
• Ability to facilitate interdisciplinary interaction and present complex understanding with colleagues and a wider audience
  
• Ability to write for publication, present research proposals and results
  
• Ability to document and share analysis workflows to support transparency and reproducibility.
  
• Ability to interact with senior NHS and University colleagues
  
• Good attention to detail, and able to maintain intense concentration for a sustained period of time despite interruptions
  
• Self‑motivated with excellent organisational skills and a desire for innovation
  
• Critically appraise scientific and technical information
  
• Ability to teach colleagues about analysis of large data sets
  
• Ability to perform processing of medical imaging data
  

Additional Requirements

• 
  
• The ability to understand and behave at all times, towards patients, visitors and colleagues according to the Trust values of safe, kind, excellent.
  

Employer certification / accreditation badges

The postholder will have access to vulnerable people in the course of their normal duties and as such this post is subject to the Rehabilitation of Offenders Act 1974 (Exceptions) Order 1975 (Amendment) (England and Wales) Order 2020 and as such it will be necessary for a submission for Disclosure to be made to the Disclosure and Barring Service to check for any previous criminal convictions.

Dr Patrick Deegan
Consultant Metabolic Physician

Liz Morris - Lead Specialist Nurse

Informal enquiries directed to Dr. Patrick Deegan () are encouraged.