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ABOUT THE ROLE

The main objective of this post-doctoral research associate position is to develop and implement advanced machine learning methods for constructing latent spaces for multi-modal data integration, connecting molecular and imaging patterns favourable for checkpoint blockade immunotherapy (CBI) in non-small cell lung cancer (NSCLC). Additionally, the post-holder will work on using causal representation learning to develop counterfactual explanations for imaging biomarkers in NSCLC CBI.
The post-holder will be a core scientist on the project, which is led by Dr Mitch Chen, MRC Clinician Scientist and Consultant Radiologist.

WHAT YOU WOULD BE DOING

You will carry out research programmes in machine learning as applied to lung cancer precision oncology, including:

• Constructing and implementing latent space representations for imaging-molecular data integration.
• Exploring causal representation learning to develop counterfactual explanations for imaging biomarkers in NSCLC CBI.
• Developing explainable imaging biomarkers for NSCLC CBI, and testing them in multi-centre datasets from collaborating centres and publicly available sources.
• Investigating the identifiability of causal-based frameworks.

You will work with molecular readouts (spatial transcriptomics and mutational panel data) from lung cancer tissue, liquid biopsy samples, clinical, and multi-modal medical imaging data.