Many UK researchers lack the hands-on skills to use artificial intelligence tools responsibly, and a new training programme aims to fix that. AI and machine learning are transforming how science gets done—from analysing patient scans to predicting climate patterns—but most researchers were never trained to use them. This skills gap means valuable data goes unexplored, and poorly applied AI can produce misleading results. The programme, delivered through the established DiRAC Training Academy, offers free, self-paced modules covering core techniques like deep learning and generative models. Interactive Jupyter Notebooks let participants work with real scientific datasets, while recorded expert lectures show how AI has been successfully applied in fields such as astronomy, materials science, and fluid dynamics. Live drop-in sessions and discussion forums provide one-on-one guidance and encourage cross-disciplinary collaboration. If successful, the programme will create a larger, more diverse pool of UK researchers who can confidently integrate AI into their work. That could accelerate discovery across physics, engineering, biology, and beyond—turning AI from a specialist niche into a standard scientific tool. The training is not about immediate commercial applications; it is about building the fundamental human capability to use powerful new methods responsibly.
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AI/ML Training (DiRAC) This proposal presents a structured training programme designed to equip UK researchers with essential skills in the artificial intelligence (AI) and machine learning (ML), technologies that are rapidly transforming scientific research. While AI/ML offer powerful tools for solving complex problems and driving innovation, many researchers lack the technical expertise required for effective and responsible use. This skills gap poses risks to research integrity and scientific discovery. To address this and drive the wider adoption of AI capabilities across research fields, the proposed initiative will deliver a modular, self-paced training programme through the established DiRAC Training Academy online platform. The curriculum will focus on core machine learning techniques, including deep learning and generative models, and will be inclusive and freely accessible to the UK research community. Interactive Jupyter Notebooks using real scientific datasets will provide hands-on experience and promote awareness of high-quality, well-governed data sources. The programme will also feature a series of expert-led “deep-dive” sessions. These ~2-hour recorded lectures will explore real-world applications of AI/ML within specific scientific domains, reinforcing theoretical knowledge through practical demonstrations of successful research codes and methods. To further support participants, the final phase of the programme will include live virtual drop-in sessions and interdisciplinary discussion forums. These will offer one-on-one expert guidance, facilitate cross-disciplinary collaboration, and encourage co-creation of innovative research approaches. Overall, this initiative will build a confident, skilled, and diverse research community, accelerate the integration of AI as a standard scientific tool, and foster long-term innovation across UKRI domains.
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