Research Associate, Medical Imaging AI - Robust Validation

University of Sheffield; Sheffield; Hybrid; GBP 42,000 - 52,000; Full time

Job summary

The University of Sheffield invites applications for a two-year Grade 7 Research Associate position in CAPTURE-PH to translate and refine RAIDA cardiothoracic CT AI across large, clinically characterised pulmonary hypertension cohorts. You will audit, retrain and technically harden existing RAIDA CT assets so they operate robustly across ASPIRE and PHINDER, tackling scanner heterogeneity and data quality.

Applicants should have a PhD or equivalent in computer science, machine learning or medical

Qualifications

• PhD or equivalent in computer science, ML, medical imaging or related field.
• Strong Python/PyTorch skills and 3D medical imaging experience.
• Experience with robust model validation and reproducible research software.
• Familiarity with nnU-Net/MONAI and explainable AI is advantageous.

Responsibilities

• Audit, refine, retrain and harden RAIDA CT assets for cross-centre robustness.
• Develop and evaluate segmentation, feature-extraction and prediction pipelines for biomarkers.
• Apply domain adaptation, semi-supervised learning and human-in-the-loop refinement.
• Link imaging outputs with clinical data to support CAPTURE-PH questions.
• Establish reproducible multi-centre validation workflows.

Skills

Python

PyTorch

3D medical imaging

Model validation

Domain adaptation

Semi-supervised learning

Explainable AI

Reproducible research

Education

PhD or equivalent

Tools

nnU-Net

MONAI

Job description

The University of Sheffield invites applications for a two-year Grade 7 Research Associate position in CAPTURE-PH to translate and refine RAIDA cardiothoracic CT AI across large, clinically characterised pulmonary hypertension cohorts. You will audit, retrain and technically harden existing RAIDA CT assets so they operate robustly across ASPIRE and PHINDER, tackling scanner heterogeneity and data quality.

Applicants should have a PhD or equivalent in computer science, machine learning or medical