ELITE-S (http://elite-fellowship.eu/) is a Marie Skłodowska-Curie COFUND Action for new inter-sectoral training, career development and mobility fellowship programme. Each fellowship will be based at the ADAPT Centre in one of its universities (Trinity College Dublin, Dublin City University, University College Dublin, TU Dublin) The programme will train the next generation of leaders in ICT Standards by offering 16 prestigious two-year fellowships to experienced researchers (ER) for incoming mobility across two calls over the 60 months duration of the programme. Fellowships will be offered in the four core research areas: - Regulations and Compliance - Content Analytics and Content Preservation - Data and Process Management - Interoperability And applicants will have the freedom to choose among these research topics where they’ll develop ground-breaking research to become research leaders of the future. Fellows will conduct their research in one of the academic partners and also take part in a secondment period in the industry, maximizing their benefit from the academy expertise, industry collaboration opportunities and representation opportunities at Standards Developing Organisations (SDOs).

We are recruiting for a PhD to work with us in the Marie Curie Initial Training Network project called HELICAL. The post is for 1 x early stage researcher (ESR) who will undertake a three year PhD studentship as part of the H2020-MSCA-ITN-2018 HELICAL (Co-ordinator: Professor Mark Little, Trinity College Dublin, TCD). HELICAL is an EU funded Marie Curie Innovative Training Network (ITN) with 17 Academic Partners and nine Non-Academic Partners. This post is to be based in ADAPT at the School of Computer Science and Statistics, Trinity College Dublin and will be supervised by Professor Declan O'Sullivan . The project is entitled Scalable semantic data architecture for study of chronic disease . The successful applicant will adapt an existing geo-spatial semantic data architecture to support RDF-based data management mechanisms to promote research while protecting privacy. The existing infrastructure allows scalable incorporation of data streams deriving environmental archives (weather, pollution and infectious agents), near-patient app-derived information and RKD registry data. The applicant will set up RDF-based scalable infrastructure to uplift and integrate data, to export data to analytic tools, and support pre-processing for deeper patient set analysis using machine learning approaches, with a view to comparing app-derived data between patients suffering a vasculitis flare and those remaining in remission.