This research investigates why many organizations fail to implement AI effectively, focusing on readiness rather than technology. In automotive after-sales services, it identifies gaps between systems and AI ambitions. The study develops a framework aligning people, processes, and capabilities, helping organizations achieve sustainable and successful AI adoption.

This research investigates asthma’s underlying mechanisms, focusing on airway fibrosis and the extracellular matrix. Using Raman spectroscopy, researchers generate molecular “barcodes” of lung tissue. Artificial intelligence is then applied to analyze complex data, aiming to identify key biological drivers of asthma and move beyond temporary treatments toward deeper understanding and potential long-term solutions.

This research uses AI to detect subtle interactions between the Higgs boson and muons at the Large Hadron Collider. By refining large datasets, it aims to uncover how particles acquire mass at smaller scales. Confirming this interaction would deepen understanding of the Higgs field and fundamental physics.

This research develops drones with soft robotic arms capable of safely grasping and transporting objects in challenging environments. By combining predictive modelling with visual feedback, it overcomes control challenges associated with soft materials. The work advances intelligent, adaptive aerial robotics for applications such as emergency delivery and hazardous environments.