University of Edinburgh
FundRef: 501100000848 , 501100002754 , 501100009147 , 100010949 , 501100000635 , 100014372 , 501100005698 , 100008932 , 501100015685
ISNI: 0000000419367988
RRID: RRID:SCR_011637
Wikidata: Q160302
FundRef: 501100000848 , 501100002754 , 501100009147 , 100010949 , 501100000635 , 100014372 , 501100005698 , 100008932 , 501100015685
ISNI: 0000000419367988
RRID: RRID:SCR_011637
Wikidata: Q160302
University of Edinburgh
Funder
8,291 Projects, page 1 of 1,659
Open Access Mandate for Publications and Research data assignment_turned_in Project2020 - 2024Partners:Stockholm University, UCL, Leipzig University, EPFL, UV +4 partnersStockholm University,UCL,Leipzig University,EPFL,UV,EPFZ,University of Edinburgh,UOXF,DLRFunder: European Commission Project Code: 860100Overall Budget: 4,185,720 EURFunder Contribution: 4,185,720 EURClimate change is one of the most urgent problems facing mankind. Implementation of the Paris climate agreement relies on robust scientific evidence. Yet, the uncertainty of non-greenhouse gas forcing associated with aerosol-cloud interactions limits our constraints on climate sensitivity. Radically new ideas are required. While the majority of forcing estimates are model based, model uncertainties remain too large to achieve the required uncertainty reductions. The quantification of aerosol cloud climate interactions in Earth Observations is thus one of the major challenges of climate science. Progress has been hampered by the difficulty to disentangle aerosol effects on clouds and climate from their covariability with confounding factors, limitations in remote sensing, very low signal-to-noise ratios as well as computationally, due to the scale of the big (>100Tb) datasets and their heterogeneity. Such big data challenges are not unique to climate science but occur across a wide range of data science applications. Innovative techniques developed by the AI and machine learning community show huge potential but have not yet found their way into climate sciences – and climate scientists are currently not trained to capitalise on these advances. The central hypothesis of IMIRACLI is that merging machine learning and climate science will provide a breakthrough in the exploration of existing datasets, and hence advance our understanding of aerosol-cloud forcing and climate sensitivity. Its innovative training plan will match each ESR with supervisors from climate and data sciences as well as a non-academic advisor and secondment and provide them with state-of-the-art data and climate science training. Partners from the non-academic sector will be closely involved in each of the projects and provide training in a commercial context. This ETN will produce a new generation of climate data scientists, ideally trained for employment in the academic and commercial sectors.
more_vert assignment_turned_in ProjectFrom 1984Partners:University of EdinburghUniversity of EdinburghFunder: National Institutes of Health Project Code: 3F32NS006925-02S1more_vert assignment_turned_in ProjectPartners:Centro integrado de formación profesional CIFP SAN JORGE, City of Glasgow College, ACADEMIA, IZOBRAZEVANJE IN DRUGE STORITVE DOO, University of Edinburgh, Gemeinnuetziges Berufsfoerderungswerk GmbH +3 partnersCentro integrado de formación profesional CIFP SAN JORGE,City of Glasgow College,ACADEMIA, IZOBRAZEVANJE IN DRUGE STORITVE DOO,University of Edinburgh,Gemeinnuetziges Berufsfoerderungswerk GmbH,GOSPODARSKA ZBORNICA SLOVENIJE CENTER ZA POSLOVNO USPOSABLJANJE,EIfI-Tech.,Berufsschule PinkafeldFunder: European Commission Project Code: 2019-1-UK01-KA203-061523Funder Contribution: 312,936 EURThe construction industry is experiencing an unprecedented period of rapid change across Europe, wrought by technological advances and socio-economic drivers. As the industry responds to emergent challenges including labour skills gaps, improving energy consumption and resource efficiency, digitising and modernising processes, and improving productivity; the need for a paradigm shift from traditional to modern methods of construction has been never been greater. Offsite and modular methods of construction (OSM) have been utilised in different ways across Europe and have proven to be cleaner, safer and more productive than traditional building methodologies. Recent advances in technological processes have brought offsite methodologies into the mainstream, with the associated benefits of addressing the key issues identified above as well as providing a route to lower cost, affordable housing stock. Investment in a transnational collaboration presents an opportunity to accelerate the pan-European response to the education and skilling needs of the construction industry. The proposal is predicated on the sharing of best practice, commonality in benchmarking and standardising educator training, sharing of effective pedagogical approaches and resources, and the development of a European network of professionals working and teaching within the field of OSM education. The proposal is positioned within the field of higher education with a focus on developing advanced-level technical skills, thus our references to advanced vocational education and training (AVET) is synonymous with higher education.The project aims to modernise the delivery of off-site and modular (OSM) construction advanced vocational education & training (AVET) by developing an innovative training model and joint curriculum that provides a framework to benchmark the quality of delivery within the partner nations. Specific emphasis will be placed upon the digitisation of key processes as well as key agendas including decarbonising activity and improving energy efficiency, workplace safety practices and supporting a diverse and equal workforce.The objectives of this project are:1.Design and implement an innovative training model and competency framework that modernises the delivery of off-site and modular construction AVET by Feb 2022.2.Engage learners across the EU in innovative courses in the area of off-site and modular construction by Feb 2022. (2000 individual engagements through our platform)3.Develop and disseminate a suite of open online educational resources to support AVET practitioners in the delivery of industry standard and emerging off-site and modular construction practices by Feb 2022. (8 modules with 40 hours of learning content each; webinars and joint teacher training)4.Support local businesses, particularly small and micro businesses, to improve awareness and adoption of offsite and modular construction practices within their construction activity. (60 businesses engaged throughout project)Seven partners from five different countries (Spain, UK, Germany, Austria and Slovenia) across the European Union have joined this project. Our partners include AVET institutions in all countries as well as a European institute settled in Germany. Many of these partners have extensive links to the construction industry in our partner countries and an enormous amount of relevant experience. Most institutes have a long history of working on European projects.After initially reviewing the European landscape in off-site and modular (OSM) construction we will create a new course model and competency framework based on our research. This model will then be used to create course content that will be distributed online. We will disseminate this content on a dedicated platform and will host specific teacher training, industry engagement and multiplier events. The project proposes the development of open education resources, co-designed and co-delivered by the partners, which incorporate innovative teaching practices, high quality learning resources and a wide range of digital assets. The products of all intellectual outputs outlined within this application will be free at the point of access, open source, compliant with interoperability standards and available in the languages of the represented partner nations (English, German, Spanish and Slovenian). All digital content will be available on our platform EMIC-GEM.eu and will be optimised for transfer to other common digital platforms.The project will have a large impact on a number of construction AVET teachers, AVET institutions, construction companies and learners by offering new material, upskilling teachers, modernising course delivery and engaging businesses throughout the process.
more_vert assignment_turned_in Project2006 - 2008Partners:University of EdinburghUniversity of EdinburghFunder: UK Research and Innovation Project Code: ES/E004091/1Funder Contribution: 119,279 GBPAbstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
more_vert assignment_turned_in Project2003 - 2005Partners:University of EdinburghUniversity of EdinburghFunder: Wellcome Trust Project Code: 070638more_vert
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