ALFIE
DAS contributes to European work on trustworthy and ethical AI, supporting responsible innovation and governance for advanced systems.
Responsible AI research programmeResearch & Innovation
Applied research for systems that need evidence before scale.
DAS works with enterprises, public bodies and research consortia to move advanced AI concepts through validation, assurance, pilot deployment and commercialisation.
Research areas
Research themes built for practical adoption.
Dedicated research themes explain how DAS turns advanced ideas into validated, governable and adoptable systems.
Responsible AI & AI Assurance
Trustworthy AI patterns for transparent, explainable and human-centred systems.
Digital Twins & Simulation
Simulation-backed approaches for testing operational choices before deployment.
Cyber-Physical Systems
Research and engineering across connected physical assets, sensors, data and software.
High-Risk R&D Commercialisation
Commercialisation pathways for advanced research where uncertainty, evidence and adoption risk must be managed.
Grants, Consortia & Public Innovation Programmes
Applied technical contributions for funded R&D programmes, public innovation and multi-partner consortia.
DataSim
A flagship example of research commercialisation.
DataSim models UK rail routes and supports large-scale simulation for timetable optimisation and decision support.
- Developed through Innovate UK-backed R&D and later private investment.
- Built for complex operational data, repeatable scenarios and optimisation at scale.
- Relevant to wider simulation, digital twin and AI assurance programmes.
Projects
Named research and innovation programmes.
These project records are prepared for public-facing validation and can be expanded as more detail is approved.
DataSim: A Machine Learning-powered simulation tool for rail timetable optimisation
A rail simulation and optimisation project that developed DataSim as a high-complexity software platform for modelling routes, timetables and operational choices.
10090014Reliable Railway Data Summaries for enhanced Decision Support and Productivity
An R&D project focused on improving the reliability, usability and decision value of railway data summaries for operational productivity.
10126997