Research Associate: Medical Imaging AI - CAPTURE-PH RAIDA

Dunhillmedical; Sheffield; Hybrid; GBP 41,000 - 46,000; Full time

Benefits offered by this job

Hybrid working

Generous pension

Staff discounts

Job summary

University of Sheffield is seeking a Grade 7.5 Research Associate in Computer Science for CAPTURE‑PH RAIDA to refine existing CT imaging‑AI assets and make them robust across datasets. You will work with radiologists and clinical scientists to improve segmentation, feature extraction and prediction pipelines, ensuring reproducible validation workflows.

The role requires a PhD in a relevant field, strong Python/PyTorch skills and experience with medical‑image data (DICOM/NIfTI).

Qualifications

• PhD in computer science, ML, medical imaging, biomedical engineering or related field.
• Strong practical experience in deep‑learning for medical image analysis.
• Proficiency in Python with PyTorch; able to develop and optimize research code.
• Experience with medical imaging data, DICOM/NIfTI pipelines and QA.
• Experience validating models across sites/datasets and external hold‑outs.

Responsibilities

• Refine RAIDA CT imaging‑AI assets for CAPTURE‑PH across ASPIRE/PHINDER cohorts.
• Audit pipelines for lung/parenchyma, cardiac chambers, vessels; identify limitations.
• Develop supervised deep‑learning methods for segmentation and classification.
• Improve generalisability across scanners and protocols; apply domain adaptation.
• Design robust training, testing and external validation workflows.
• Collaborate with radiologists, clinicians and statisticians on imaging biomarkers.
• Create reproducible, version‑controlled software and inference workflows.
• Link imaging biomarkers with clinical data for CAPTURE‑PH analyses.

Skills

PhD in relevant field

Deep‑learning for medical imaging

Python

PyTorch

DICOM/NIfTI workflows

Generalisation across sites

Versioned pipelines

Education

PhD in computer science/ML/medical imaging

Tools

nnU-Net

MONAI

Vision transformers

Grad-CAM

Job description

Research Associate: Medical Imaging AI - CAPTURE-PH RAIDA

The University of Sheffield is a remarkable place to work. Our people are at the heart of everything we do. Their diverse backgrounds, abilities and beliefs make Sheffield a world-class university.

We offer a fantastic range of benefits including a highly competitive annual leave entitlement (with the ability to purchase more), a generous pensions scheme, flexible working opportunities, a commitment to your development and wellbeing, a wide range of retail discounts, and much more. Find out more about our benefits (opens in a new window) and join us to become part of something special.

Overview

We are seeking a Grade 7 Research Associate in Computer Science for a two-year post within CAPTURE-PH, a multi-centre programme using chest CT and artificial intelligence (AI) to improve the diagnosis, phenotyping and prognostic assessment of pulmonary hypertension associated with interstitial lung disease (PH-ILD). The post will build on RAIDA, the group’s existing cardiothoracic imaging-AI programme, rather than developing a new platform from scratch.

RAIDA brings together AI methods for automated analysis of cardiac chambers, pulmonary vessels and lung parenchyma, with explainable outputs and expert review. Your primary task will be to refine, retrain and technically harden these existing CT assets so that they work reliably across the CAPTURE-PH datasets, initially ASPIRE and PHINDER and subsequently other approved cohorts.

You will address variation in scanners, acquisition protocols and disease phenotypes; undertake quality control and failure analysis; improve segmentation, feature extraction and prediction pipelines; and establish reproducible validation workflows. You will work closely with radiologists, pulmonary vascular clinicians, clinical scientists and statisticians to link RAIDA‑derived imaging biomarkers with right‑heart catheterisation, clinical measurements and outcomes. The aim is to deliver robust, generalisable research tools and validated imaging outputs that can support CAPTURE-PH analyses and future clinical translation.

Main duties and responsibilities

• Refine, adapt and maintain existing RAIDA CT imaging‑AI assets for CAPTURE‑PH, with initial focus on robust operation across the ASPIRE and PHINDER cohorts.
• Audit existing RAIDA pipelines for lung parenchymal, cardiac chamber, large‑vessel and pulmonary vascular analysis; identify technical limitations and prioritise changes required for heterogeneous CAPTURE‑PH imaging.
• Develop and optimise supervised deep‑learning methods for segmentation, feature extraction, classification and prediction, including retraining or fine‑tuning existing models using expert‑reviewed contours and labels where required.
• Implement methods to improve generalisability across scanners, vendors, reconstruction methods and acquisition protocols, including appropriate domain‑adaptation, semi‑supervised or related approaches where these add value.
• Design and perform rigorous training, testing and hold‑out/external validation, including repeatability, calibration, subgroup performance, bias assessment, quantitative segmentation metrics and clinically relevant diagnostic/prognostic performance measures.
• Undertake systematic image review, quality control and failure‑mode analysis; work with clinical experts to understand errors, curate difficult cases and feed corrections back into model development.
• Create reproducible, version‑controlled research software and inference workflows that can process DICOM‑derived imaging at scale, generate structured quantitative outputs and operate within approved secure research computing environments.
• Link RAIDA‑derived CT biomarkers with right‑heart catheterisation, lung function, walk testing, NT‑proBNP, MRI and outcome data to support CAPTURE‑PH analyses of PH diagnosis, severity, prognosis and treatment‑responsive phenotypes.
• Support human‑in‑the‑loop review and explainability of model outputs, including integration with approved image‑review workflows such as MIM where appropriate, and contribute to documentation needed for reproducibility and future translation.
• Contribute to project meetings, milestone reporting, manuscripts, conference presentations and collaborative work with CAPTURE‑PH and RAIDA investigators, while following research governance, data‑security and quality‑assurance requirements.
• Carry out other duties, commensurate with the grade and remit of the post.

Person Specification

The selection process will take place following the closing date. This will consist of an interview and a short technical/research presentation or practical discussion relevant to medical‑imaging AI. We will let candidates know if they have progressed to the selection stage following the closing date. If you need any support, equipment or adjustments to enable you to participate in any element of the recruitment process you can contact [e-mail].

Criteria

Essential or desirable

Stage(s) assessed at

A PhD (submitted, achieved or equivalent research/professional experience) in computer science, machine learning, medical imaging, biomedical engineering, data science or a closely related discipline.

Essential

Application/interview

Strong practical experience developing deep‑learning methods for medical‑image segmentation, classification, regression or quantitative image analysis, preferably with 3D CT data.

Essential

Application/interview/test

Proficiency in Python and a modern deep‑learning framework such as PyTorch, with the ability to develop, debug, profile and optimise research code independently.

Essential

Application/interview/test

Experience working with medical‑imaging data and associated formats/workflows, including DICOM and/or NIfTI, preprocessing, metadata handling and data‑quality assessment.

Essential

Application/interview

Experience adapting or validating AI models across heterogeneous datasets, including external/hold‑out validation, failure analysis and assessment of generalisability across sites, scanners or acquisition protocols.

Essential

Application/interview

Ability to build reproducible, version‑controlled computational pipelines and to work effectively with secure research computing or high‑performance computing environments.

Essential

Application/interview

Strong analytical problem‑solving skills and the ability to take ownership of a defined technical work programme, prioritise tasks and deliver agreed milestones over a two‑year project.

Essential

Application/interview

Effective communication and collaborative working skills, including the ability to work with radiologists, clinicians, clinical scientists, statisticians and other AI researchers.

Essential

Application/interview

Experience with medical‑image segmentation frameworks/architectures such as nnU‑Net, MONAI, vision transformers, or related approaches, and/or explainable image‑classification models such as DenseNet/Grad‑CAM.

Desirable

Application/interview

Knowledge of thoracic CT, pulmonary hypertension/ILD, domain adaptation or semi‑supervised learning, or human‑in‑the‑loop clinical AI validation.

Desirable

Application/interview

Further Information

Grade

Grade 7.5 - 7.9

Salary

£41,064 - £46,049

Work arrangement

Full‑time (hybrid/flexible working options considered where compatible with secure data and computing requirements)

Duration

Fixed‑term for 24 months

Line manager

RAIDA / CAPTURE‑PH academic lead

Direct reports

None

Right to work in the UK

If you do not currently hold the right to work in the UK, you can find more information here to help determine your visa eligibility. Additional guidance is also available on the U K Visa & Immigration website .

For informal enquiries about this job contact Andrew Swift, Professor of Cardiothoracic Radiology, [e-mail].

Next steps in the recruitment process

The selection process will take place following the closing date. This will consist of an interview and a short technical/research presentation or practical discussion relevant to medical‑imaging AI. We will let candidates know if they have progressed to the selection stage following the closing date. If you need any support, equipment or adjustments to enable you to participate in any element of the recruitment process you can contact [e-mail] .

Our vision and strategic plan

We are the University of Sheffield. This is our vision: sheffield.ac.uk/vision (opens in new window).

What we offer

• A minimum of 41 days annual leave including bank holiday and closure days (pro rata) with the ability to purchase more.
• Flexible working opportunities, including hybrid working for some roles.
• Generous pension scheme.
• A wide range of discounts and rewards on shopping, eating out and travel.
• A variety of staff networks, providing opportunities for social interaction, peer support and personal development (for example, Race Equality, LGBT+, Women’s and Parent’s networks).
• Recognition Awards to reward staff who go above and beyond in their role.
• A commitment to your development, with access to learning and mentoring schemes.
• A range of generous family‑friendly policies
  • paid time off for parenting and caring emergencies
  • and more

We are a research university with a global reputation for excellence. Our ideas and expertise change the world for the better, making a real difference to society. We know that when people come together with different views, approaches and insights it can lead to richer, more creative and innovative teaching and research and the highest levels of student experience. Our University Vision ( www.sheffield.ac.uk/vision ) outlines our commitment to building a diverse community of staff and students that recognises and values the abilities, backgrounds, beliefs and ways of living for everyone.