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CAMaCS

Amplifying impact

CAMaCS operates as a single point of reference to support academics to establish and drive research towards impact with the long-view in mind. This can be done via multiple strategic approaches, for example:

  • Promoting strong pan-campus engagement, matching skill set to opportunity in the spaces of data-driven, mathematical and AI modelling, building teams and hosting discussion to explore some of the many interdisciplinary grant calls with fast turnarounds.
  • Supporting dialogue with industry and pursuing different forms of research collaborative relationships, whether via PhD funding, long-term collaborative project funding, consultancy contracting, or other forms of knowledge exchange.

CAMaCS has two flagship schemes to add value to impact facing research:

  • Innovation Research Associates (InRAs):InRAs are a new type of researcher working to support multiple projects in the space of “impactful research”. InRAs serve as a research intermediary for hand-in-hand impact driven research collaborations between academics and external partners. This provides the gift of time and research support that would otherwise come as relatively costly investment. Through their principal role in undertaking quantitative/AI modelling, as well as acting as a scientific intermediary between mathematical/AI researchers and other academics/stakeholders/partners, InRAs are included in grant proposals and industry-funded projects.
  • Impact Fellowship Scheme (IFS):CAMaCS recognises that there are times in the lifecycle of impact delivery which need the specific focus of academic leads, e.g. “getting impact over the finish line”. The IFS is a robust scheme that will allow colleagues to bid for time buy-out at critical junctures along the pathway to impact. Details of this scheme will follow soon.

Examples of our engagement areas include:

  • Industrial applied mathematics: including applications of fluid dynamics, solid mechanics, and PDEs to solve problems held by diverse partners with physical modelling needs.
  • Distributed socio-technical systems: including applications of network science, agent-based systems, and nonlinear dynamics to challenges faced by partners in e.g. transportation systems, urban science, financial regulation, energy grids, and social sciences.
  • Applied statistics/data science: including impactful and ethical applications of data science to social and technological problems such as those in the Data Science for Social Good initiative.