Deep Generative Learning for Cone Beam Computed Tomography: Goal: to design deep generative models that improve CBCT image quality while enforcing geometric and pathological integrity.

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Learning Inverse Models for Cone Beam Computed Tomography Reconstruction: Goal: to learn deep inverse models to infer high quality 3D and 4D CBCT from measured projections that optimally exploit the existing knowledge from the physical acquisition processes

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Deep Learning Geometry for 3D Medical Image Registration: Goal: to learn models that optimally register the varying geometries in pairs or series of images of deformable anatomy

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Interactive Deep Learning for Medical Segmentation: Goal: to replace static segmentation models with interactive model based approaches that can, therefore, integrate the feedback provided by the experts interactively and can minimise or eliminate even violations of the constrains are necessary

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Learning to Forecast for Adaptive Radiation Therapy: Goal: to learn forecasting models that predict dose distributions for a given anatomy and series of future anatomies and associated dose distributions in adaptive radiation treatment

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Reinforcement Learning for Radiation Treatment Plan optimisation: Goal: to improve and accelerate treatment plan optimization using novel reinforcement learning algorithms for guiding radiation in adaptive radiotherapy treatment