Grid-Scale Energy Storage Technologies are the undeniable elements of sustainable energy systems for several reasons such as stabilizing variable energy generation of solar and wind farms, facilitating sector coupling, optimizing resource utilization, improving energy security and resiliency, etc. However, the widespread deployment of such systems is currently very challenging due to major technological, economic, and policy gaps. These issues could only be addressed through cutting-edge research and innovative developments driven by skilled researchers and specialists with multi-lens visions on all critical aspects. Established around a rigorously tailored plan of action, RESTORATIVE brings together the global leaders of relevant fields and industrial actors across Europe to launch a multidisciplinary platform to address the gaps by several breakthrough solutions and training 17 doctoral fellows in different disciplines. To cover the diverse factors influencing the real-world implementation of novel energy systems, the project's thorough work plan is centered around research and PhD training in "mechanical, chemical, and material engineering for technological innovations", "power engineering, and computer sciences for addressing security, reliability, and operation bottlenecks", and "energy economics, energy policy, and environmental sciences for mitigating regulatory, policy and sustainability barriers". Since state-of-the-art research consistently highlights the superiority of thermo-mechanical solutions among all energy storage classes for grid-scale applications, RESTORATIVE is dedicated to Thermo-Mechanical Grid-Scale Energy Storage Systems. Notably, the project has a comprehensive and meticulously planned program of training and career development for doctoral students, including specialized courses, schools, workshops, academic and industrial secondment, entrepreneurship skill-building, etc.

Cybersecurity is one of today's most challenging security problems for commercial companies, NGOs, governmental institutions as well as individuals. Reaching beyond the technology focused boundaries of classical information technology (IT) security, cybersecurity includes organizational and behavioural aspects of IT systems and also needs to comply to the currently actively developing legal and regulatory framework for cybersecurity. For example, the European Union recently passed the Network and Information Security (NIS) directive that obliges member states to get in line with the EU strategy. While large corporations might have the resources to follow those developments and bring their IT infrastructure and services in line with the requirements, the burden for smaller organizations like local public administration will be substantial and the required resources might not be available. New and innovative solutions that will help local public administration to ease the burden of being in line with cybersecurity requirements are needed. For example, cooperation and coordination is one of the major aspects of the NIS and EU cybersecurity strategy. An enabling technology for cooperation and coordination is cyber situational awareness and information sharing of cyber incidents. In this project we propose a cybersecurity situational awareness solution for local public administrations that, based on an analysis of the context provides automatic incident detection and visualization, and enables information exchange with relevant national and EU level NIS authorities like CERTs. Advanced features like system self-healing based on the situational awareness technologies, and multi-lingual semantics support to account for language barriers in the EU context, are part of the solution.

CS-AWARE-NEXT aims to provide improved cybersecurity management capabilities to organizations and local/regional supply networks. Such organisations and networks operate in a highly dynamic cybersecurity environment, and are required to comply with prevailing European legislation such as the network and information security (NIS) directive. The way such organizations approach cybersecurity increasingly needs to be more dynamic and more collaborative, building on a shared situational awareness of potential cybersecurity issues relevant to the organisations and networks in question. To achieve this, CS-AWARE-NEXT has identified several focus areas to be addressed: (a) Improved organisational policy support to enable organizations to deal better with the dynamic nature of cybersecurity. (b) Greatly enhanced cooperation/collaboration within the organization and with external actors, such as those comprising the local/regional supply chain. (c) Better integration of threat intelligence in operational cybersecurity management using innovative AI approaches and techniques. (d) Much improved disaster recovery/business continuity, integrated in operational cybersecurity management. (e) Elevated evidence collection and information sharing with relevant actors on the multi-level European cybersecurity framework. (f) Improved capacity for enabling organizations to assess their security status in comparison with other relevant actors through benchmarking and profiling. CS-AWARE-NEXT builds on the awareness, cybersecurity information sharing, and system self-healing capabilities of the CS-AWARE platform developed during the H2020 project of the same name. The integration of the advanced capabilities of CS-AWARE-NEXT will enable organizations and dependent supply networks to be much more effective and efficient in their use of cybersecurity platforms like CS-AWARE, supporting their day-to-day cybersecurity risk and incident management operations.

CRIMSON aims to provide a next-generation bio-photonics imaging device based on vibrational spectroscopy, with the potential to revolutionise the study of the cellular origin of diseases allowing for novel approaches towards personalised therapy. We will employ label-free broadband coherent Raman scattering (CRS) extended to the fingerprint region, in combination with artificial-intelligence spectroscopic data analysis, for fast cell/tissue classification with unprecedented biochemical sensitivity. We will develop a hyperspectral CRS microscope for 3D quantitative imaging of sub-cellular compartments in living cells and organoids. High acquisition speed will enable the observation of intra- and inter-cellular dynamic changes by time-lapse imaging. We will simulate future in-vivo studies and demonstrate the capability to image inside the body, realizing an innovative CRS endoscope and applying it to ex-vivo thick tissue slides. CRIMSON relies on the development of new compact ultrafast lasers, innovative broadband CRS detection schemes and advanced spectral analysis routines. To validate the CRS platform, we will investigate three open biological questions related to cancer, as paradigmatic examples of the complexity and heterogeneity of cellular diseases. The results will have profound societal impacts, improving patients’ quality of life and reducing public healthcare costs. CRIMSON brings together a multidisciplinary team of world-leading academic organizations, biomedical end users and innovative SMEs, with vertical integration of all required skills. CRIMSON will bridge the gap between research and product development, increasing the TRL and making CRS a user-friendly, robust and cost-effective mainstream tool for a vast biological research community. Commercial exploitation by the participating SMEs, including a biomedical equipment manufacturer, will create a competitive advantage in the European biophotonics-related market for microscopes and R&D tools.

The GREYDIENT innovative training network aims at training a next generation of Early Stage Researchers (ESR) to fully sustain the ongoing transition of European personal mobility towards safe and reliable intelligent mobility systems via the recently introduced framework of grey-box modelling approaches. One of the main challenges that we currently face in this context is the integration of the data captured from the plenitude of sensors that are involved in a particular road-traffic scenario, ranging from monitoring car-component loading situations to power network-reliability estimations. The aim is to fully exploit the potential of merging these data with advanced computational models of components and systems that are widely available in industry in order to fully assess the momentarily safety. Grey box models are an answer to this pressing issue, as they are aimed at optimally integrating (black-box) data driven machine learning tools with (white-box) simulation models to greatly surpass the performance of either framework separately. However, the training of professional profiles in Europe who combine knowledge and experience in state-of-the-art data-driven black box and numerical white box approaches with expertise in methods for reliability and safety estimation is scarce. Therefore, GREYDIENT will train its ESR’s in a wide spectrum of fields, including the modelling, propagation and quantification of the relevant variabilities, the application of big data and machine learning methods, as well as the optimal combination of data-driven approaches with numerical models. All our ESR’s will obtain a PhD from an internationally respected University, build experience in communicating and disseminating their work, applying their research skills in a non-academic context and receive in-depth training in transferable skills such commercialization, collaboration and entrepreneurship. This training will be organized in close cooperation with key industry stakeholders.