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Data, Digital and AI

10 September 2026

Edinburgh, United Kingdom

University of Edinburgh

AIMday
Edinburgh
2026
Data, Digital and AI
Registration for researchers

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Registration for researchers

Registration is now open and will close on 26 August 2026.

Step 1 – Register your personal details

Please register your personal details below. This information will be used to contact you regarding your participation in the AIMday event.

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Describe your research *This field is required.

Would you like to attend lunch, and if so, do you have any dietary preferences or allergies? *This field is required.

Step 2 – Select your challenges

Here is a list of challenges for the event. You can click the plus sign of a challenge to expand it and read more. Select the challenges you would like to discuss at the AIMday event. You may choose several challenges and you can drag and drop them to prioritise them according to your preferences. The challenge at the top of the list has the highest priority.

Select and prioritise challenges *This field is required.

5. How can organisations securely combine and utilise data across sectors while preserving privacy, provenance and trust? — Talking Medicines

7. How could emerging technology improve the incentives for competitive development of AI on small-medium data centres in Scotland, which can make better use of green energy, reuse their waste heat within communities, and keep raw data closer to its owners? — Scottish Government

8. How can we create human-machine contexts, particularly when using AI, that are most conducive to using AI to enhance learning and cognition rather than create incentives for cognitive offloading/laziness? — ITZA Media Ltd.

12. How can we use a natural language system (AI, ML, NLP, etc.) to review financial reports and earnings calls from listed companies and identify obfuscation, red flags, and potential accounting issues hidden in the text? — Vinley (NextGenValueInvesting Ltd)

14. Create a balance sheet optimisation engine that drives the reallocation of interest-earning assets to business units and financial instruments that generate the highest return (optimising Net Interest Income levels) — HSBC Bank PLC

16. How can recurring, reusable sub-circuit patterns ("gadgets") be automatically discovered from large collections of quantum circuits, so that a circuit optimiser can learn its own library of high-value building blocks rather than relying on hand-crafted rules? — Qinara

18. Can we trust LLM as a judge in production, and know when it has quietly stopped being trustworthy? — Amazon

19. How can organisations best design and implement effective AI guardrails and governance mechanisms that promote fair and responsible AI usage, mitigate undesirable model behaviour, and protect against malicious or unintended user interactions, when introducing AI at scale? — Barclays

20. As agentic systems gain access to a wider range of enterprise data pertaining to individuals, how can this data be managed with the concept of 'memory' whilst honouring the usage profile for the data e.g. when can it be used and when must it be forgotten? — IBM

22. Can machines learn to recognise manufacturing excellence in fibre based packaging? — Kongsberg Precision Cutting Systems

23. Blue Energy wants to use AI to support nuclear power design and construction projects. However, they will be subject to commercial and legal security constraints, and we require very high integrity of AI output. How should we structure our approach to achieve these? — Blue Energy

33. What combination of technical evidence would allow a technical platform operator to prove that a customer's data and models never left its sovereign, secure infrastructure? — HFD Datavita Limited

36. Bottom-up demand and flexibility forecasting for distribution networks. Distribution networks are becoming harder to forecast as demand, embedded generation, electric vehicles, heat pumps, batteries, and flexible loads all interact across different locations and time horizons. Operators increasingly need forecasts not only at grid-supply-point or substation level, but also at more granular points across the distribution system, including smart meters, feeders, substations, SCADA nodes, and controllable assets. How can AI methods produce reliable bottom-up forecasts of demand and flexibility availability across a distribution network, using heterogeneous data sources with different resolutions, quality levels, ownership boundaries, and uncertainty characteristics? — ENODA Ltd

39. How can generative AI agents be used robustly to enable government to take a systemic view of policy? — Scottish Government

42. When a neuro-symbolic system encounters an entity or failure mechanism outside its formal ontology, what should it do, and how can that behaviour be made more auditable? Can a system admit provisional, uncertain knowledge without forfeiting the determinism that makes it certifiable in the first place? — Euryan

46. In an (historic) environment where accuracy, evidence and source material are foundational is there a role for AI in our records? — Historic Environment Scotland

47. How can we use multi-modal AI to automatically extract technical information from circuit diagrams and OEM product manuals to build the digital electrical architecture of a grid-scale battery? — 3E

50. How can artificial intelligence be used to develop predictive white-box models from intellectual-property-protected manufacturer models that can be applied across a range of power system studies without infringing proprietary information? — SSEN TRANSMISSION

53. How can AI, computer vision and sensor fusion be used to develop the next generation of fish-counting systems for hydroelectric environments? — SSE

57. How can we detect and govern Unmanaged AI Agent Identities in Enterprise Environments? — NCC Group

To participate in AIMday you must register for the event. By registering for AIMday, please be aware that your information will be processed, in accordance with GDPR guidelines, to facilitate this AIMday. Read more what requested information is used for.

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