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New Archive of Formal Proof Website

    A project involving Carlin MacKenzie, James Vaughan and Jacques Fleuriot has resulted in a re-design and re-implementation of the Archive of Formal Proofs, which is a collection of proof libraries, examples, and larger scientific developments, mechanically checked in the theorem prover Isabelle. The revamped website (https://www.isa-afp.org) is now live and will serve the Isabelle community across the world. The Edinburgh team worked with Fabian Huch of TU Munich to integrate their work into the AFP infrastructure.

    Programme Co-ordinator post available on Artificial Intelligence for Multiple Long-Term Conditions Programme

      We are looking for an experienced researcher (Grade 8, salary £42,149-£50,296), with a PhD in Artificial Intelligence, machine learning or data science, to coordinate and support the delivery of our core AI research programme and well as overarching programme management. This a joint post between the School of Medicine and the School of Informatics at the University of Edinburgh.

      Artificial Intelligence and Multimorbidity: Clustering in Individuals, Space and Clinical Context (AIM-CISC) is a 3 year, £3.9 million research programme funded by the National Institute for Health Research (NIHR) Artificial Intelligence for Multiple Long-Term Conditions Programme. AIM-CISC is led by a multidisciplinary team from The University of Edinburgh, University College London and NHS Lothian. This includes clinical and genetic researchers studying complex multimorbidity, public partners, social scientists researching wider social and spatial determinants of health and care, and informatics and data science academics with AI expertise across multiple domains including natural language processing, machine-learning including multilayer network analysis, and applied AI.

       

      Three postdoctoral Research Associate posts available in Artificial Intelligence for Multiple Long-Term Conditions Programme

        Applications are invited for 3 Research Associates in Machine Learning for Health, Network Science, Knowledge Representation and AI to work within the Artificial Intelligence and Multimorbidity: Clustering in Individuals, Space and Clinical Context (AIM-CISC) programme in the School of Informatics and Usher Institute, University of Edinburgh.

        Artificial Intelligence and Multimorbidity: Clustering in Individuals, Space and Clinical Context (AIM-CISC) is a 3 year, £3.9 million research programme funded by the National Institute for Health Research (NIHR) Artificial Intelligence for Multiple Long-Term Conditions Programme. AIM-CISC is led by a multidisciplinary team from The University of Edinburgh, University College London and NHS Lothian. This includes clinical and genetic researchers studying complex multimorbidity, public partners, social scientists researching wider social and spatial determinants of health and care, and informatics and data science academics with AI expertise across multiple domains including natural language processing, machine-learning including multilayer network analysis, and applied AI. The research areas are broadly as follows:

        1.Network Science for the analysis and prediction of multimorbidity.

        2. Machine Learning for clustering of complex multimorbidity.

        3. Knowledge Representation for Medical Artificial Intelligence

         

        Our proposal on AI and Multimorbidity: Clustering in Individuals, Space and Clinical Context (AIM-CISC) receives funding from the NIHR!

          The National Institute for Health Research (NIHR) has decided to fund our proposal on Artificial Intelligence and Multimorbidity: Clustering in Individuals, Space and Clinical Context (AIM-CISC). The project, worth £3.9M over 3-years, will employ around 10 postdoctoral researchers across Informatics, the Usher Institute, the Roslin Institute, GeoSciences and SSPS.

          The overall programme will be led by Bruce Guthrie (PI), with Jacques Fleuriot as the AI Lead in Informatics. The other members of the Informatics team are Sohan Seth and Valerio Restocchi.

          Some project details:

          Long-term conditions are health issues which persist over years, with many people having more than one long-term condition (e.g. having both diabetes and asthma). This is known as multimorbidity and often seriously affects how well people feel and what they are able to do. The aim of the project is to use Artificial Intelligence techniques — spanning areas such as machine learning, network science, knowledge graphs and process mining — along with social science and health service research methods, to create a better understanding of common, disabling patterns of multimorbidity and help improve the quality and safety of care.