Research Associate: Medical Imaging AI - Capture-Ph RAIDA

Research Associate:
Medical Imaging AI - CAPTURE-PH RAIDA

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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.

• 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…