The aim of this cosmology proposal is to study the contents, history and evolution of our Universe, using the distribution of matter on large scales. I propose to study new methods to identify and catalogue rare voids and superclusters in the large-scale structure of the Universe and to study their statistical, morphological and dynamical properties. I will use these studies to propose new observables and new techniques of data analysis based on these structures, and to use this knowledge to test theories of gravitation, dark energy and the initial conditions of the early Universe. Interest in cosmic voids in particular has increased greatly in recent years, as they have been suggested as extremely competitive probes of cosmology, according to some estimates significantly out-performing other methods. Yet there remain unsolved theoretical difficulties in the modelling of voids and even a lack of consensus on how to identify them and how to relate theory and observation. Even less is currently known about superclusters. These are among the important issues I propose to address. My proposal consists in roughly equal parts of: analysis of the latest high-quality survey data which will available from the Sloan Digital Sky Survey (SDSS) and the Dark Energy Survey (DES); analysis of the state-of-the-art Jubilee ISW and weak lensing simulations run on the Juropa supercomputer in Germany; and theoretical work to combine insights from data and simulation to improve our understanding of cosmology. The proposed work is to be carried out in collaboration with leading experts in the respective fields from across the European Community, in particular in the United Kingdom, Spain and Germany.

Weight reduction and cost savings have driven composites research towards a number of recent high profile achievements. The increased use of anisotropic AL/CFRP/Ti stacks in aircraft structures has in turn created enormous challenges for the industry due to the difficulties that arise from drilling these heterogeneous stack materials. The project “European and Chinese Platform for Stacked Aero-Structure Drilling Process and Equipment (ECSASDPE)” focuses on the staff exchange between the partners of EU and China, and the development of key enabling techniques and equipment for theorbital drilling process of stacked AL/CFRP/Ti. It meets the objectives and requirements of the Marie Skłodowska-Curie Actions: Research and Innovation Staff Exchange (RISE), by establishing multiple bridges between European and Chinese institutions. The ultimate goal of ECSASDPE is to set up a long-term international and inter-sector collaboration consortium through research and innovation staff exchanges between 8 world-recognised institutions in the cutting-edge research area of high value manufacturing with promising applications in scientific and engineering sectors. The synergistic methodologies achieved by ECSASDPE will serve as the building blocks of the stacked composite materialmachining mechanism, equipment design, process monitor and control, and machining quality metrology and characterisation and scale up application, and thus enhance the leading position of the consortium for the scientific and technological progresses in high value manufacturing. This project is divided into six inter-related work packages: (1) Setup of knowledge base and road mapping; (2) Fundamentals of drilling process; (3) Key techniques for Equipment development; (4) System integration and performance verification; (5) Dissemination and exploitation, and (6) Project management. The work packages integrate all activities that will lead to the accomplishment of all the project objectives within 66 months.

The past four years have witnessed dramatic discoveries surrounding the birth of gravitational wave astronomy. By their nature, gravitational waves are ideal probes with which to test the laws of gravity – something currently under scrutiny due to unresolved questions about the dark sector of the universe. In this proposal I lay out an ambitious campaign to determine the behaviour of gravity over cosmological distances, using the upcoming surge of gravitational wave data. I will achieve this by developing the burgeoning technique of `Statistical Host Identification’ of gravitational wave sources. This new tool will enable me to test gravity using hundreds of future detections of binary black holes at high redshifts, even without direct redshift information – thus removing a major obstacle for gravitational wave cosmology. I will phrase my constraints in terms of model-independent parameters that quantify physically viable deviations from General Relativity, making my results applicable to virtually any dark energy or extended gravity model. In this way, I can validate or eliminate the space of theories in current literature. To model the distribution of gravitational wave events and their host galaxies, I will construct an approximate simulation that operates with generalised, model-independent gravitational laws – the first ever simulation to do this. This tool enables me to additionally use information about gravity from non-linear scales of cosmological structure. This regime is virtually untouched by current comparable work, and is a prime target for the next generation of galaxy surveys. My key objectives are: i) To develop the calculations and software tools needed to apply gravitational wave Statistical Host Identification, in theories of gravity beyond General Relativity; ii) To use these tools to obtain powerful new constraints on extended gravity models, thereby confirming or ruling out a leading candidate explanation for the nature of dark energy.

The AI-ARC proposal presents a highly innovative and user-friendly artificial intelligence (AI) based platform known as the Virtual Control Room (VCR). Due to the vast amounts of information collected the potential for information overload is real. This reality can complicate the operational picture; reduce situational awareness and often results in delayed and impaired decision-making. On the other hand, areas such as the Arctic Sea suffer from a lack of communication, surveillance data and rescue assets and without action taken to address these vulnerabilities, the consequences are potentially dramatic in terms of accidents, pollution, border infringements and criminal activities. The AI-ARC VCR supports all these challenges by applying AI, machine-learning and virtual reality (VR) technologies to filter numerous validated and statistical data streams and databases to a user-friendly interface. The VCR improves situational awareness by assisting end users to customize a “smart” operational picture. The VCR will permit users to specify their preferences in terms of threat levels, abnormal behavior, interoperability and risk management by flagging detected anomalies with confidence and providing threat or risk levels according to a predefined model based on user preferences. This means that users can create awareness for their own purposes that reflects their needs without increasing their workload. AI-ARC‘s principal objectives align fully with the H2020 BES-SU-open, and are of crucial relevance to it. The Virtual Control Room (VCR) has the power to greatly improve maritime situational awareness, decision-making, communication, available rescue resources, and thus the safety of all maritime actors, particularly in the Arctic Sea. Furthermore, the enhanced communication and collaboration provided by AI ARC’s innovative technology encourages, and enables further development of symbiotic services and fosters much needed Arctic cooperation.