Funding awarded for European Training Network in AI

The European Commission has awarded €4m in funding to support a new European Training Network (ETN) on “Monitoring Large-Scale Complex Systems” (MOIRA), as part of its Horizon 2020 framework. The funding allows Oxford University's Department of Engineering Science to join a network of European Universities and industrial collaborators supporting research and training for the next generation of experts in AI.

The focus of MOIRA is to develop novel AI-based methods for handling complex, real-world sensor data, with the aim of performing predictive inference in sectors such as healthcare, aerospace, and manufacturing. In addition to a four-year long research agenda, the programme involves funding for a cohort of 15 “ETN Fellows”, who undertake their PhDs in AI with consortium members.

Oxford's involvement is led by Reuben Fellow David Clifton, Professor of Clinical Machine Learning in the Computational Health Informatics (CHI) Lab, at the Institute of Biomedical Engineering.

Speaking of the award, David said: “I’m especially pleased to see that a major emphasis is the very generous funding that the cohort of Fellows receives to undertake their PhDs and travel frequently between partner institutions. The ability to gain a truly international PhD, study with leading AI-focussed groups across Europe, and build experience across important application domains is unique to European Training Networks.”

“Of course, it is especially important to see initiatives such as this going ahead given the UK’s relationship with our European colleagues, and in such a strategically important area as AI. Schemes such as this bring us closer, with the real winners being the next generation of AI experts who are trained within the programme.”

European Training Networks help researchers gain experience of different working environments while developing transferable skills. The ETN on Monitoring Large-Scale Complex Systems (MOIRA) will promote cooperation among top European universities, research institutes, wind-turbine and plant operators, and industrial stakeholders with an expertise in mechanical engineering, computer science, signal processing, vibrations, inverse problems, operations and maintenance, data analytics and networks. 

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