CYBELE generates innovation and create value in the domain of agri-food, and its verticals in the sub-domains of PA and PLF in specific, as demonstrated by the real-life industrial cases to be supported, empowering capacity building within the industrial and research community. Since agriculture is a high volume business with low operational efficiency, CYBELE aspires at demonstrating how the convergence of HPC, Big Data, Cloud Computing and the IoT can revolutionize farming, reduce scarcity and increase food supply, bringing social, economic, and environmental benefits. CYBELE intends to safeguard that stakeholders have integrated, unmediated access to a vast amount of large scale datasets of diverse types from a variety of sources, and they are capable of generating value and extracting insights, by providing secure and unmediated access to large-scale HPC infrastructures supporting data discovery, processing, combination and visualization services, solving challenges modelled as mathematical algorithms requiring high computing power. CYBELE develops large scale HPC-enabled test beds and delivers a distributed big data management architecture and a data management strategy providing 1) integrated, unmediated access to large scale datasets of diverse types from a multitude of distributed data sources, 2) a data and service driven virtual HPC-enabled environment supporting the execution of multi-parametric agri-food related impact model experiments, optimizing the features of processing large scale datasets and 3) a bouquet of domain specific and generic services on top of the virtual research environment facilitating the elicitation of knowledge from big agri-food related data, addressing the issue of increasing responsiveness and empowering automation-assisted decision making, empowering the stakeholders to use resources in a more environmentally responsible manner, improve sourcing decisions, and implement circular-economy solutions in the food chain.

SEAwise will address the key challenge preventing implementation of a fully operational European Ecosystem Based Fisheries Management: the need to increase fisheries benefits while reducing ecosystem impact under environmental change and increasing competition for space. The SEAwise network of stakeholders, advisory bodies and scientists will co-design key priorities and approaches to provide an open knowledge base on European Social-Ecological Fisheries Systems. SEAwise will innovate the prediction of social indicators of small-scale fisheries, coastal communities, carbon footprint and human health benefits. Using these indicators in fisheries models will help give advice on economically effective and socially acceptable governance under climate change, productivity changes, and the landing obligation. SEAwise will link the first ecosystem-scale assessment of maritime activities’ impacts on habitats with the fish stocks they support. Using ecosystem effects on fishing, including environmental metrics, density dependence, predation, stock health indicators and habitat extent will improve stock productivity predictions. Estimating effects of fishing on sensitive species, benthic habitats, food webs, biodiversity and litter allows evaluation of the mutual consistency of objectives for ecological and social systems. Multispecies-multifleet models will provide ecosystem forecasts of the effect of fisheries management measures. SEAwise will identify the simplest possible combination of management measures and investigate portfolio diversification as an approach for managing ecosystem resilience and climate adaptation. SEAwise tools and courses for ICES, GFCM, stakeholders and decision makers will ensure that these methods can be used directly in Mediterranean, western European, North Sea and Baltic Sea waters. The predictions will inform an online advice tool highlighting stock- and fisheries-specific social and ecological effects and management trade-offs.

The aim of this project is to specifically look at the development of advance structural damping simulations based on subcomponent qualification with different material basis (carbon vs glass, balsa wood vs foam cores). The PhD would work on a structural model simulating the energy dissipation associated to the damping of structural vibrations. This modelling should be performed in the model domain such that Rayleigh damping values can be estimated for wind turbine load simulations.

The aim of this PhD is to contribute to the understanding of the economic impacts of offshore wind activity in the UK, by reconciling the ambitions for the future of the industry with current measures of economic contribution. By examining the current metrics for economic contribution of renewable energy, and reconciling the differences across such measures, a narrative for the industry can be developed. Detailed engagement with stakeholders across the sector will help to develop a qualitative understanding of the potential impact from the future trajectory of offshore renewable energy developments. Understanding the link between offshore development and its economic contribution is vital to avoid a gap emerging between the ambitions of the Sector Deal and existing economic measures for the industry.

The project is a qualitative study into the HE experience of an under-researched 'non-traditional' group of students, 'estranged' students. 'Estranged' students are defined by the Office For Fair Access (OFFA 2017) as those who have 'no communicative relationship with either of their living biological parents and often their wider family networks as well'. Narratives from 20-30 'estranged' students will be collected using Bourdieu's lens and concepts, exploring among others 'estranged' students' routes into HE, prior experiences and friendship networks.