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Applications for the Manchester Prize are now open, with a deadline 12:00 GMT on 1 February 2024. To help to ensure coherence, avoid duplication and maximise the strength of UoM applications, Digital Futures are operating a light touch process of internal review and support, with senior input via a pan-University panel.

If you propose to lead on a application or if you propose to play a substantive role in one . If you have any questions please email digitalfutures@manchester.ac.uk. (If you are not lead please complete as much detail as possible; we recognise that you may not have all the required information at this stage.)

The first Manchester Prize will be awarded to the most innovative and impactful AI solution which demonstrates social benefit by overcoming challenges in the fields of energy, environment and infrastructure.

Solutions could include:

• Reducing energy costs for consumers by using AI to model household energy use and identify targeted interventions, such as retrofitting and replacement.
• Supporting emergency service response by bringing together a range of spatial data about the road and built environment to improve last mile routing.
• Improving the response to extreme weather conditions by using AI and earth observation data to predict areas vulnerable to flooding, or to support better real-time spatial data of events such as wildfires and flash floods.
• Reducing disruption to public services through predictive modelling of infrastructure resilience, with automated scheduling of maintenance, such as deploying teams to fix potholes or other traffic obstructions.
• Enhancing food security by using earth observation and soil data to monitor and improve farming productivity and crop yield.
• Improving efficiency and reducing resource consumption in manufacturing by using AI to optimise or automate energy-intensive processes.

These are examples of how Manchester Prize think you could address the overarching statement (but you’re welcome to think of your own).

Manchester Prize encourage solutions that demonstrate advances in technical capabilities such as generalisation, uncertainty quantification, interpretability, data-efficient AI and physics-based AI – but other approaches are welcome too.