Six programmes are currently active, each led by a member or affiliate of the lab. Projects run under the Bio-AImagiQ initiative and draw on the shared imaging facility.
Fully developing D2IM by integrating additional AI strategies, advanced XCT segmentation models and the power of large language models. The goal is a model that reads a tomographic volume and returns the internal strain field directly, at a fraction of the cost of conventional digital volume correlation.
Applying advanced AI models to analyse, predict and optimise the hierarchical self-assembly of nanoparticles into macroscale soft biomaterials — connecting formulation choices to the structure that emerges from them.
Exploring the unique spectral signature of biological tissues and developing advanced AI models for healthcare, building on DiffSpectralNet and MedDiffHSI toward clinically useful tumour detection.
Developing D2IM-based tools that give the industrial partner a technology to identify the nature of defects in bio-composites and inform the manufacturing process.
Developing quantum-native representations of biological tissue images, enabling efficient AI operations such as classification, segmentation, measurement and multimodal data fusion directly in the quantum space.
A cross-disciplinary collaboration between academics at the Faculty of Engineering and Science, investigating a novel multi-dimensional AI sonification approach to design and optimise bone biomimetic materials — blending high-resolution imaging, AI sonification, computational modelling and additive manufacturing.