Research Associate, Medical Imaging AI & CT Analytics

Diversity Dashboard; Sheffield; Hybrid; GBP 41,000 - 46,000; Full time

Benefits offered by this job

41 days annual leave

Hybrid working

Generous pension

Discounts and rewards

Job summary

The University of Sheffield is seeking a Grade 7 Research Associate in Computer Science to refine and advance the RAIDA CT imaging‑AI assets for CAPTURE‑PH, ensuring robust operation across cohorts. You will work with radiologists and clinicians to enhance segmentation, feature extraction and prediction pipelines while maintaining reproducible, secure research workflows.

Applicants should have a PhD and strong Python/PyTorch experience in medical imaging, with track record in cross‑site

Qualifications

• PhD (submitted, achieved or equivalent) in computer science, machine learning, medical imaging or related discipline.
• Strong practical experience developing deep‑learning methods for medical‑image segmentation, classification, regression or quantitative image analysis, preferably with 3D CT data.
• Proficiency in Python and a modern deep‑learning framework such as PyTorch, with the ability to develop, debug, profile and optimise research code independently.
• Experience working with medical‑imaging data and formats/workflows (DICOM/NIfTI), preprocessing, metadata handling and data quality assessment.
• Experience adapting or validating AI models across heterogeneous datasets, including external/hold‑out validation and generalisability assessment.
• Ability to build reproducible, version controlled computational pipelines and work in secure research computing environments.
• Strong analytical problem solving, ownership of technical work programme, and milestone delivery.
• Effective communication and collaboration with radiologists, clinicians, researchers.

Responsibilities

• Refine, adapt and maintain RAIDA CT imaging‑AI assets for CAPTURE‑PH across ASPIRE and PHINDER cohorts.
• Audit pipelines for lung, cardiac, vascular analysis; identify limitations and changes for heterogeneous imaging.
• Develop and optimize supervised deep‑learning methods for segmentation, feature extraction and prediction.
• Improve generalisability across scanners and protocols using domain adaptation or semi‑supervised approaches.
• Design and execute robust training, testing, validation, including bias assessment and clinical performance metrics.
• Conduct systematic image review, quality control and error analysis; feed corrections into model development.
• Create reproducible, version‑controlled research software and inference workflows for DICOM outputs at scale.
• Link RAIDA imaging biomarkers with clinical data to support CAPTURE‑PH analyses and translation.
• Support human‑in‑the‑loop review and explainability; document reproducibility for future use.
• Contribute to meetings, milestones, manuscripts, and collaborations while following governance and data security requirements.
• Carry out other duties commensurate with the grade and remit of the post

Skills

Deep-learning methods

Medical-imaging AI

Python PyTorch

Education

PhD in CS/ML/Medical imaging

Tools

DICOM/NIfTI

nnU-Net MONAI

Job description

The University of Sheffield is seeking a Grade 7 Research Associate in Computer Science to refine and advance the RAIDA CT imaging‑AI assets for CAPTURE‑PH, ensuring robust operation across cohorts. You will work with radiologists and clinicians to enhance segmentation, feature extraction and prediction pipelines while maintaining reproducible, secure research workflows.

Applicants should have a PhD and strong Python/PyTorch experience in medical imaging, with track record in cross‑site