CT Imaging AI Research Associate

The University of Sheffield; Sheffield; Hybrid; GBP 41,000 - 46,000; Full time

Benefits offered by this job

41 days annual leave (plus extras)

Pension scheme

Flexible working options

Discounts and staff benefits

Job summary

The University of Sheffield is seeking a Grade 7.5–7.9 Research Associate in Computer Science for CAPTURE‑PH, a two‑year project applying AI to pulmonary hypertension with CT imaging. You will refine, retrain and harden existing RAIDA CT assets for robust, cross‑dataset performance.

You will work with radiologists, clinicians and statisticians to ensure reliable outputs and reproducible validation workflows, across ASPIRE and PHINDER cohorts.

Qualifications

• PhD or equivalent in CS/ML/medical imaging or closely related field.
• Strong experience developing deep‑learning methods for medical‑image segmentation, classification, regression or quantitative image analysis.
• Proficiency in Python and PyTorch with ability to develop and debug research code independently.
• Experience with medical‑imaging data and formats (DICOM/NIfTI), preprocessing, metadata handling and data quality assessment.
• Experience 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 HPC environments.
• Excellent communication and collaboration with radiologists, clinicians and AI researchers.
• Familiarity with models/architectures such as nnU‑Net, MONAI, vision transformers or DenseNet/Grad‑CAM.

Responsibilities

• Refine and maintain RAIDA CT imaging‑AI assets for CAPTURE‑PH across ASPIRE/PHINDER cohorts.
• Audit RAIDA pipelines for lung parenchymal, cardiac, vascular analysis; prioritise changes for CAPTURE‑PH imaging.
• Develop supervised deep‑learning methods for segmentation, feature extraction, classification and prediction.
• Improve generalisability across scanners, vendors and acquisition protocols.
• Design training, testing and external validation with repeatability, calibration and bias assessment.
• Collaborate with radiologists and clinicians to interpret errors and feed corrections back into models.
• Create reproducible, version‑controlled research software and inference workflows.
• Link RAIDA biomarkers with clinical data to support CAPTURE‑PH analyses and translation.
• Contribute to meetings, reports, manuscripts, and governance.

Skills

Deep learning

Python

PyTorch

Medical imaging data

DICOM/NIfTI

Model generalisation

Reproducible pipelines

Communication with clinicians

Education

PhD in computer science / medical imaging

Tools

nnU-Net

MONAI

Job description

The University of Sheffield is seeking a Grade 7.5–7.9 Research Associate in Computer Science for CAPTURE‑PH, a two‑year project applying AI to pulmonary hypertension with CT imaging. You will refine, retrain and harden existing RAIDA CT assets for robust, cross‑dataset performance.

You will work with radiologists, clinicians and statisticians to ensure reliable outputs and reproducible validation workflows, across ASPIRE and PHINDER cohorts.