To reduce climate impact of aviation, decarbonisation is a major challenge. Current combustion chambers are burning hydrocarbon fuels, such as kerosene or more recently emerging SAF products. Hydrogen is also considered today as a promising energy carrier but the burning of hydrogen creates radically new challenges which need to be understood and anticipated. HESTIA specifically focuses on increasing the scientific knowledge of the hydrogen-air combustion of future hydrogen fuelled aero-engines. The related physical phenomena will be evaluated through the execution of fundamental experiments. This experimental work will be closely coupled to numerical activities which will adapt or develop models and progressively increase their maturity so that they can be integrated into industrial CFD codes. Different challenges are to be addressed in HESTIA project in a wide range of topics: - Improvement of the scientific understanding of hydrogen-air turbulent combustion: preferential diffusion of hydrogen, modification of turbulent burning velocity, thermoacoustics, NOx emissions, adaptation of optical diagnostics; - Assessment of innovative injection systems for H2 optimized combustion chamber: flashback risk, lean-blow out, stability, NOx emission minimisation, ignition; - Improvement of CFD tools and methodologies for numerical modelling of H2 combustion in both academic and industrial configurations. To this end, HESTIA gathers 17 universities and research centres as well as the 6 European aero-engine manufacturers to significantly prepare in a coherent and robust manner for the future development of environmentally friendly combustion chambers.

FORSEE posits that the advancing technical capabilities of AI applications require a clear understanding of what successful AI means - for society as a whole - together with the conditions of possibility for successful AI. New AI technologies are sites of negotiation and contestation. Different groups, based on their social positions, develop diverse and possibly conflicting ideas on what constitutes “success”. Expanding visions that define AI strictly in terms of technological and economic efficiency, FORSEE aims to develop a nuanced and enriched notion of success that will guide future AI applications and policy efforts. To achieve this, FORSEE draws from the social construction of technology to engage with three categories of stakeholders: a) institutional actors, b) “lifeworld” stakeholders (Civil Society Organizations representing gendered perspectives and Digital SMEs) and, c) the broader public. Then, FORSEE inquires into the impact of AI applications on economy, society and sustainability as well as on their alignment with EU values and strategic priorities. These interconnected research projects will illuminate a broader understanding of success that rests upon conflict resolution, empowerment of stakeholders, and alignment with fundamental rights and the goal of sustainable development. Based on these research findings, FORSEE will (i) develop a novel approach to AI governance that can guarantee more successful AI applications for society as a whole, including (ii) a new evaluative framework for assessing current and future AI applications, and (iii) a new prototype for registering risk and negative impacts. Through these outputs, FORSEE highlights and analyses existing successful AI applications to strategically enhance capabilities of our stakeholders and policymakers to address future risks and opportunities. Updating the SME sector’s understanding of success is a particular condition for the EU to retain a leading position in the AI landscape.

In 2017, on a world scale the total number of individuals with chronic kidney disease, acute kidney injury, and those on renal replacement therapy exceeded 850 million, a truly concerning figure that is twice the estimated number of people with diabetes worldwide and >20 times higher than the number of individuals affected by AIDS/HIV worldwide. The socioeconomic impact of kidney disease is huge and is anticipated to even further grow in the coming years. Awareness of the magnitude and the risks of this condition have remained low at the population level. Therefore, kidney disease has been, until recently, largely overlooked by health authorities and governments in most countries with as the result accumulation of major unmet needs in personalized medicine in kidney disease. The aim of PICKED (PersonalIzed medicine in Chronic KidnEy Disease) is to equip a generation of 10 doctoral candidates (DC) with interdisciplinary skills for the development of pathways to implement personalized medicine in chronic kidney disease and their complications on the level of its detection, progression and treatment. We anticipate that these DCs will significantly contribute to the increasing possibilities to stratify patients with kidney disease and the development of successful intervention procedures with potential to substantially impact the lives of over 10% of the European population. PICKED will realize this aim by coordinating the efforts of 10 beneficiaries and 8 associated partners across 7 European countries and 3 sectors, with lead supervisors covering a large range of disciplines ranging from biomarker-research to research into the legal, ethical and quality of life aspects for the implementation of personalized medicine in kidney disease.

The world population will increase from 7.5 to 9.7 billion by 2050, boosting agricultural demand and adding pressure on natural resources. Although agriculture remains the main activity in many countries, the decline in agriculture’s share of total production and employment is observed. Agricultural investments and technological innovations are needed to boost productivity. Romania, with an agricultural capacity of 14.7 million hectares from which 6.8 million hectares are not or under-exploited, could play a major role in the strategic autonomy and food security and sustainability of Europe. Romania’s agricultural capacity is heavily underexploited due to obsolete technology, soil fragmentation/erosion, desertification and difficulty in accessing funds. Artificial intelligence (AI)-based systems and applications have a significant potential for agriculture. The Copernicus EU Programme, offering free access to accurate Earth Observation (EO) data provided by the Sentinel satellites, opens incredible possibilities for future research and AI-based applications for the purpose of sustainable development of agriculture in Europe. The AI4AGRI project aims at creating a dedicated research center for AI in EO for the agricultural sector. The research excellence will be achieved through dense networking activities with two top research institutes in France and Italy in AI and EO. AI4AGRI research center will become a reference to train young scientists in the domain of AI for agriculture, providing maps of vegetation status for Romanian farmers based on EO data using AI. AI4AGRI will develop administrative and management skills for research and innovation. To achieve these goals, AI4AGRI twinning will enhance the networking activities between partners through a strategy comprising of joint research, short-term staff exchanges, expert visits and short-term training, joint summer schools and workshops, as well as conference attendance, dissemination and outreach activities.

Stars are the source of radiation, chemistry, and life in the Universe. Models of how stars live are key ingredients in planetary, astrophysical, and cosmological research. A star is a hot plasma rotating around an axis. Small stars like the Sun rotate slowly but bigger ones with more mass rotate faster, shaping them as flattened spheroids. Yet, current stellar models simplify the flattening or treat stars as spheres during their lives, using 1 spatial and 1 time dimension (1+1D). Rotation and magnetism induce transport processes in 3 spatial dimensions, which change over time, requiring a 3+1D treatment. Current age-dating of stars is done from 1+1D models, with uncertainties up to 1000%. Accurate ages of stars are the dominant missing ingredient to understand stellar and planetary evolution, the emergence of life, and the chemistry in our Universe. 4D-STAR will answer the fundamental question of how rotating spheroids evolve in time and build up their chemistry during their lives. We will develop a new 3+1D theory of stellar rotation for flattened spheroids evolving over millions to billions of years, from birth to death. Lifting stellar models to 3+1D can only be done now, using asteroseismic data of thousands of stars in all life phases. Such data reveal nonradial oscillations, or starquakes, allowing us to infer internal stellar rotation, magnetism, chemistry, and the ages of stars with 10% accuracy. 4D-STAR will provide open-source modules to compute the evolution of rotating magnetic stars in 3+1D, calibrated to asteroseismic observables of single stars and stars in binaries and clusters. 4D-STAR brings a paradigm shift based on mathematical modelling, astrophysics, and computational science. Its breadth, challenges, and goals require a transdisciplinary integration of four teams led by an asteroseismologist, a theoretician specialised in transport, a hydrodynamicist, and a stellar evolution software developer, each with proven track records.