Funding call: New funding programme to help deploy AI for research and innovation

(5 July 2022) The Accelerate Programme in collaboration with C2D3 is offering small grants for Cambridge University researchers pursuing innovative applications of AI, in research or real-world contexts. Funding can support a variety of activities, including events, workshops, teaching, software development, or research, with a focus on interdisciplinary collaboration.

Successfully deploying AI to tackle real-world challenges requires effective interdisciplinary collaboration. Developing these collaborations requires time and resources to bring together potential research partners, investigate the different ways of framing a challenge, and co-design potential solutions. Scaling successful interventions might require new ways of convening, innovations in training, or novel software tools. Often this work falls outside the scope of routine funding calls.

Accelerate Science’s funding programme will help fill this gap, offering small grants that can be deployed flexibly to start or scale interdisciplinary collaborations in the use of AI for research and innovation.

Introducing the funding call, Professor Neil Lawrence said: “There’s an enormous amount of excitement about what the Cambridge University research community can achieve with wider adoption of AI. To support the collaborations vital to successfully deploying AI, Accelerate Science is providing seed funding for innovative projects that bring researchers together across disciplinary boundaries to advance the application of AI in research or real-world contexts.”

This call is open to researchers from across disciplines at the University of Cambridge wanting to use AI to accelerate their research, and who would benefit from a small amount of funding to kick-start their work or catalyse new collaborations. Applications are invited via the form at this link: https://forms.gle/1Km4BaqjvFrMPF41A by 17:00 on 9 September 2022. For further details, check out the FAQs below, or email accelerate-science@cst.cam.ac.uk.


Why have you launched this call?

As the recent AI@Cam University AI review sets out, realising the benefits of AI will require new strategies to help bring together researchers and practitioners to tackle issues of concern to science, citizens and society. This funding call is a trial to see how seed funding could help support such collaborations.

The Accelerate Programme’s mission is to drive a step-change in research and innovation at Cambridge through the application of data science in AI. As part of that work, we are keen to support the use of AI to help progress the excellent research happening across the University. Interdisciplinary collaborations are vital to this mission, and the Cambridge Centre for Data-Driven Discovery brings together researchers and expertise from across the University with the aim of enhancing interdisciplinary research.


Who can apply?

Applications are open to any postgraduate student or member of staff at the University of Cambridge, working in any discipline, including natural, physical, social, medical, and computer sciences, arts, humanities, and engineering. Proposals should be able to demonstrate a contribution to advancing the use of data science and AI for research or innovation. We’re interested in projects that have real-world application; this might include, for example, the use of AI to accelerate discovery in a specific research area, or the deployment of AI to tackle real-world challenges. We particularly encourage multidisciplinary proposals. Funding is suitable for projects or activities that can start immediately or in a short timescale. Postgraduate students may wish to check with their supervisors before applying.


What support or funding can I apply for?

This is a trial initiative, and as part of that trial we’re hoping to learn what scale of funding is useful. We hope to be able to support a number of successful applications, of up to £25,000. The amount of funding awarded will depend on the quality of the application and the number of applications received. We would encourage applications that cost less than this. If your idea costs more to deliver, it is likely better to get in touch before applying, so we can informally advise on feasibility: we have a limited budget and large funding requests will limit our ability to support other projects.

As part of developing your proposal, the Accelerate team can offer assistance via Accelerate’s Machine Learning (ML) Clinic in sense-checking technical feasibility, offering informal guidance on any machine learning-engineering aspects of the work. The ML Clinic can also provide assistance for longer-term projects where machine learning or software engineering advice would be helpful. If you’d like to access help from the Clinic please email Ahmad Abu-Khazneh (aa2382@cam.ac.uk) outlining the nature of your query. Please leave sufficient time before the funding deadline to enable response (minimum 48 hours).

As part of this funding scheme, C2D3 can offer administrative and organisational support for conferences, workshops, or other events. This could include venue arrangements, catering, hosting a webpage, delegate registration, or other operational aspects of event delivery. Depending on the nature of the project, C2D3 may also be able to offer support in the design and delivery of teaching activities.

What can funds be used for?

The focus of this programme is interdisciplinary collaboration that advances the application in research and real-world contexts. Within that remit, funding could be used for a variety of different activities, including events, workshops, conferences, teaching, software development, or research spin-up. We particularly encourage bids working across disciplines.

Spending plans should be designed in line with the University’s financial policies, for example policies on travel and accommodation. For further information, please see the Finance Division guidance, for example at: this link.

How will my proposal be evaluated?

Applications will be reviewed by a panel and evaluated using the following criteria:
  • Strength of proposal in helping enhance interdisciplinary collaboration in the use of AI.
  • Contribution of the proposed activity to generating new insights in research or new applications of AI.
  • Potential to generate longer-term impact.

What terms and conditions are associated with this funding?

The Accelerate Programme seeks to build understanding of how AI can be used to accelerate scientific discovery (covering all areas of science, arts, and humanities), including areas of opportunity, the challenges involved in deploying AI for science, and how to overcome them. To help build this understanding, funded projects are asked to provide a short blogpost that describes the question they are interested in, why this matters, and what action is needed to make progress. The Accelerate team can provide a science writer to help produce this, if required. We’ll also ask for a short evaluation explaining how the funding has benefitted your work, to help us assess the impact of this trial. Community-building is central to our work, and we hope that researchers who benefit from this funding will become involved in supporting and growing the Accelerate community!

Over what timescale should research be carried out?

This funding is particularly suited to activities that can be initiated in the near-term, with the aim of scoping, setting-up, or scaling-up interdisciplinary activities. Funds should be spent within approximately six months of award (to be confirmed as part of award agreements), and un-spent funds should be returned to the Accelerate Programme.

How can I apply?

To apply, please submit an application form by 17:00 on 9 September at: https://forms.gle/1Km4BaqjvFrMPF41A. To log in to the form, you will need to use your @cam.ac.uk email address and Raven log in details. If you are having issues accessing the form, please make sure you are first logged out of any other Google accounts (after inputting your @cam.ac.uk email, you should be redirected to a Raven log in page; if this does not happen, it may be the case that you are logged into another account).

Depending on the amount of funding requested, successful projects may be asked to submit an X5 before funding can be confirmed.

What information is required for the application form?

The application form requests the following details:
  • Questions 1-3: Primary organiser for the application (please provide the name, role, department, and email address of the primary organiser who will act as point of contact for this application).
  • Questions 4-5: Partners involved in the proposed project (please provide the following details for collaborators involved in the application: name, role, department or institution, and indicate the current status of the partnership).
  • Question 6: Project summary (briefly summarise the context for your project, the issue you will address, and your proposed intervention).
  • Question 7: Problem statement (please describe the problem or issue your activity is intended to address and the impact of that problem).
  • Question 8: Proposed solution (please describe how your proposed intervention will solve the problem set out above).
  • Question 9: Proposed approach (please describe what your intervention/activity is, how it works, and how you’ll get started).
  • Question 10: Desired outcomes (please describe what you hope to achieve with this work).
  • Question 11: Funding request (tell us how much funding you are requesting and provide a brief breakdown on headline areas of spend, for example: staffing, event organisation, bought-in services, equipment).
  • Question 12: Additional support from Accelerate or C2D3 (in this section, please advise whether you anticipate requiring any additional administrative or technical support from Accelerate or C2D3 to deliver your project, and what type of input would be helpful).
Answers to questions 6-12 should have a maximum character count of 1500 (approx. 250-300 words).

For further questions, please contact accelerate-science@cst.cam.ac.uk

  • Date published: 5 July 2022
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