Research Fellow in Medical Image Analysis and Machine Learning
Develop cutting-edge artificial intelligence algorithms for quantitative susceptibility MRI in brain iron load analysis at UCL.
Best for:Best for researchers with a PhD in Imaging Science and a strong background in AI algorithms for quantitative susceptibility MRI.
Overview
UCL's Department of Computer Science and the Hawkes Institute are seeking a Research Fellow to develop and evaluate novel artificial intelligence algorithms. The role focuses on leveraging standard MRI data to provide precise and accurate quantification of iron load distribution in the brain. The successful candidate will work in a top-ranked UK computer science department known for multidisciplinary health engineering. This position is funded for 4 months depending on the start date.
What you'll do
- Develop novel artificial intelligence algorithms for medical image analysis.
- Evaluate AI models using standard MRI data to quantify brain iron distribution accurately.
- Conduct rigorous testing and validation of quantitative susceptibility MRI (QSM) methods.
- Collaborate with multidisciplinary researchers across computational, engineering, and medical sciences.
- Contribute to research findings and documentation within the UCL Hawkes Institute.
Requirements
Degree required: PhD in Imaging Science or a closely related field
Language: English
Instruction: English
- Significant experience in developing advanced artificial intelligence algorithms
- Demonstrated expertise in evaluating AI models for quantitative susceptibility MRI (QSM)
- Strong background in medical image analysis and processing
- Proficiency in handling and analyzing MRI data
Funding & terms
How to apply
Deadline: 2026-10-31
Submit your application through the online external application portal by the specified deadline. Ensure all requested professional and academic credentials are provided.
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