Postdoctoral Researcher in Machine Learning analysis of MRI

Cambridge University Hospitals NHS Foundation Trust
Newtown
1 month ago
Applications closed
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. 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.


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


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 1,100 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.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Where to Advertise Machine Learning Jobs in the UK (2026 Guide)

Advertising machine learning jobs in the UK requires a different approach to most technical hiring. The candidate pool is small, highly specialised and in demand across AI labs, financial services, healthcare, autonomous systems and consumer technology simultaneously. Machine learning engineers and researchers move between roles through professional networks, conference communities and specialist platforms — not general job boards where ML roles compete with unrelated software engineering positions for the same audience. This guide, published by MachineLearningJobs.co.uk, covers where to advertise machine learning roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.