Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Emerging cooperative autonomous systems: Information for control and estimation (MINERVA)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Systems and Communication Engineering
Panel description: Electrical, electronic, communication, optical and systems engineering
Countries involved: Cyprus
Call: ERC CONSOLIDATOR GRANTS (ERC-2021-COG, 2021)
Start date: 2022-12-01
End date: 2027-11-30
Duration (months): 60.0
Total project cost (EUR): 1999686.0
EU funding (EUR): 1999686.0
Participants: 1
Project status: SIGNED

Nowadays, attention is drawn towards a new generation of intelligent mobile cooperative autonomous systems (CASs), built with increasingly powerful sensors that allow them to cooperate with other autonomous systems and humans, in order to accelerate the industrial ecosystem and better exploit the potential of flexible automation. The cooperation of autonomous systems, which is an emerging technology in many areas (e.g., intelligent transportation systems and factory automation) is done over a wireless network. The use of a wireless network to connect spatially distributed systems enables flexible architectures with reduced installation and maintenance costs to existing applications, while supporting the development of new applications that would otherwise be impossible. The unprecedented tight coupling between control and communication, due to the use of a shared wireless network and distributed decision making to orchestrate such systems, introduces new challenges. Current modular design approaches and incremental improvements can only provide limited performance gains and may result in inefficient solutions, which may lead to failures during practical deployments. To effectively leverage the vast space of possibilities enabled by CASs, I argue that we need to fundamentally rethink how functionally-complex CASs are interconnected and operate cooperatively. Bridging the gap between the fields of control, estimation, and information/communication theories, MINERVA will follow a bottom-up approach to develop a fundamental, yet realistic, framework to establish the foundations for real-time control, estimation, and localization in environments where autonomous systems and humans interact. MINERVA goes beyond the state-of-the-art and targets the fundamental bottlenecks of demanding systems, using methodologies fundamentally different from the existing ones, aiming towards real-time control applications with communication constraints.

Keywords: cooperative autonomous systems,wireless networked control systems,real time control and communications,co design
Advancing cooperation on the foundations of law (ALF)

Framework: Horizon Europe — Widening Participation and Strengthening the European Research Area
Programme: Widening participation and spreading excellence
Theme: Improved access to Excellence
Destination: Improved access to Excellence
Action type: Coordination and support actions
Countries involved: Serbia,United Kingdom,Portugal,Italy
Call: Twinning (HORIZON-WIDERA-2021-ACCESS-03, 2021)
Start date: 2022-10-01
End date: 2025-12-31
Duration (months): 39.0
Total project cost (EUR): 1216875.0
EU funding (EUR): 1216875.0
Participants: 4
Project status: SIGNED

There are significant disparities between EU and non-EU higher education institutions in terms of administrative and institutional capacity to successfully carry out the main task of legal education and research. Individual efforts and academic achievements of scholars from this part of Europe are usually not accompanied by the appropriate administrative and institutional structures. This situation makes it difficult for these institutions and their staff fully develop their capabilities in both academic research and legal education. ALF aims to help the Faculty of Law of the University of Belgrade - BGL (a Widening country institution) to become a widely known and sought-after top-class European research institution, by raising its academic research profile and by strengthening its research management and administrative capabilities. To this end, ALF will provide a thorough and comprehensive three-year transfer of academic and administrative foundational knowledge from three leading institutions at EU level: The University of Genoa, the University of Surrey, and the Instituto de Ciencias Juridico Politicas da Faculdade de Direito da Universidade de Lisbon. ALF fulfils five objectives of the Horizon Twinning Action: (a) it enhances the excellence capacities and resources of the BGL, thus closing the still evident research and innovation gap within Europe; (b) it enhances networking activities between the BGL and top-leading EU institutions; (c) it helps the BGL to engage in key research areas and joint activities, thus raising the research profile and attractiveness of the BGL and its staff; (d) it strengthens the BGL's research management and administrative capacity through the transfer of administrative knowledge, establishment of a specialised project office and a specialised research centre; (e) it improves creativity, mobility and scientific excellence through a novel joint research project on "Practically orientated legal fundamentals"

Keywords: philosophy of law,general jurisprudence,theory of law,language of law,logic of law,legal reasoning
Metabolic regulation of cell cleavages in early embryogenesis (MetEmbryoniC)

Framework: Horizon Europe — Excellent Science
Programme: Marie Skłodowska-Curie Actions (MSCA)
Theme: Marie Skłodowska-Curie
Destination: Postdoctoral Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Life Sciences
Countries involved: Austria
Call: MSCA Postdoctoral Fellowships 2022 (HORIZON-MSCA-2022-PF-01, 2022)
Start date: 2024-03-01
End date: 2026-02-28
Duration (months): 24.0
Total project cost (EUR): 199440.96
EU funding (EUR): 199440.96
Participants: 1
Project status: SIGNED

Multicellular animals development begins with a sequence of rapid cell cycles and divisions, named ‘cleavages’, leading to the generation of a large pool of cells, from which the embryo develops. While there are several species-specific types of cleavages, the underlying processes of cell-cycle and cytokinesis, involving characteristic large-scale cytoskeletal reorganizations, are well conserved. Yet, how these processes integrate cell-intrinsic and -extrinsic signals, adapting to the rapidly changing environment of the developing embryo, remains largely unknown. An emerging concept implies cell metabolism as an important factor regulating cytoskeletal elements and, hence, cellular function. Here, I test the hypothesis that during cleavages, specific metabolic elements play an essential role in regulating the cytoskeleton. To address this hypothesis, I developed an innovative fluxomics approach for analyzing whole-embryo metabolism, combining metabolomics, transcriptomic, and real-time bioenergetics measurements in two different embryonic cleavage pattern models: ascidians and zebrafish. By combining the Heisenberg group's expertise in biomechanics and my expertise in metabolism, I will follow three main lines of research based on the whole-embryo fluxomics data: (1) Characterizing the spatiotemporal metabolic activity in the cleaving embryo; (2) analyzing the function of specific metabolic elements in cytoskeleton regulation during cleavages; and (3) determining the extrinsic factors (temperature and oxygen levels) affecting the mechano-metabolic reactions during cleavages. I expect these findings to provide insight into mechano-metabolic mechanisms that will advance our understanding of embryonic development and shed light on disease-related processes where metabolism and cell division are highly interconnected, such as cancer.

Keywords: cellular metabolism cytoskeleton cleavage embryogenesis morphology
Integration of single-cell multi-omics data across space and time to unlock cellular trajectories (MULTIview-CELL)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Computer Science and Informatics
Panel description: Informatics and information systems, computer science, scientific computing, intelligent systems
Countries involved: France
Call: ERC STARTING GRANTS (ERC-2023-STG, 2023)
Start date: 2024-04-01
End date: 2029-03-31
Duration (months): 60.0
Total project cost (EUR): 1285938.0
EU funding (EUR): 1285938.0
Participants: 1
Project status: SIGNED

The introduction of high-throughput single-cell sequencing has produced a flood of data at the resolution of the single cell, including spatiotemporal information and different molecular facets of a cell, a.k.a. multi-omics. Their integration through MultiModal Learning (MML), aimed at combining multiple complementary views, offers great promise to understand the spatiotemporal phenotypic evolution of a cell and its molecular regulators. However, integrating multi-omics data across space and time is a huge computational challenge requiring radically new MML approaches. MULTIview-CELL will infer multimodal spatiotemporal phenotypic cell trajectories by combining back-translation, to allow the unsupervised dimensionality reduction of multimodal data, with a new Optimal Transport distance, allowing the spatiotemporal pairing of cells (Aim1). MULTIview-CELL will then pinpoint the molecular regulators of such trajectories by combining new Graph Convolutional Networks with topological evolutions and Heterogeneous Multilayer Graphs, allowing the integration of graphs inferred from multimodal data (Aim2). Finally, all developed methods will be implemented in open-source software, with an emphasis on GPU-friendly scalable computations, a unique feature among existing single-cell tools (Aim3). These core contributions will impact Machine Learning, but more importantly, will have profound biological implications. The application of the tools developed to cutting-edge single-cell data from muscle stem cells will lead to new biological hypotheses on their heterogeneity and crosstalk, to be validated through wet-lab experiments (Transversal Tasks). In addition, by allowing to answer longstanding questions on the spatiotemporal phenotypic evolution of a cell, MULTIview-CELL will catalyze the generation of crucial knowledge in fundamental biology and it will be key to preventing disease onset or therapy resistance, thus impacting health, society and economy.

Keywords: multimodal machine learning,optimal transport,graphs,single cell,multi omics
Advancing Theory and Technology for the detectiOn of dark Matter (ATOM)

Framework: Horizon Europe — Excellent Science
Programme: Marie Skłodowska-Curie Actions (MSCA)
Theme: Marie Skłodowska-Curie
Destination: Postdoctoral Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Physics
Countries involved: Germany
Call: MSCA Postdoctoral Fellowships 2024 (HORIZON-MSCA-2024-PF-01, 2024)
Start date: 2026-09-01
End date: 2028-08-31
Duration (months): 24.0
Total project cost (EUR): 202125.12
EU funding (EUR): 202125.12
Participants: 1
Project status: SIGNED

Particle physics has amassed a century of evidence pointing to the existence of massive, non-luminous Dark Matter (DM) distinct from the familiar particles of the Standard Model. We have only observed DM through its gravitational effects, leaving its microscopic properties largely unknown. Various viable models for the identity of DM particles exist and predict different properties. These uncertainties require a variety of detection strategies, relying on different technologies, each designed for a particular DM candidate. An understanding of the full implications of a specific model, as well as the broad set of possible technologies that may detect it are therefore both crucial if we wish to optimize our search for DM. This proposal utilizes my unique combined expertise in metrology and DM phenomenology, pushing forward new methods to search for DM of Ultralight masses. The rising interest in ultralight DM across the field of DM research makes this proposal extremely timely to the broader particle physics community. This work is divided into three main parts, to be pursued simultaneously. I plan to implement a novel dual-magnetometer I invented, that is able to distinguish magnetic noise from DM-induced signal at a broad set of frequencies. I also plan to continue and advance the SNIPE-Hunt collaboration, which utilizes magnetometers across the earth to search for the coherent effect of DM, both through performing new searches, and proposing future prospects. Finally, I plan to continue investigating subtle effects of DM, such as the interplay of its different interactions, as well as find new experiments that would be able to probe previously inaccessible DM parameter space using quantum magnetometry techniques.

Keywords: dark matter,quantum technology,magnetometry,phenomenology,direct detection,hot vapors,spins
Large-Scale Testing and Experimentation Facility (TEF) for Assessing, Validating, and Enhancing AI-Powered Next-Generation Energy Solutions (EnergyGuard)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Climate, Energy and Mobility
Theme: Energy
Destination: Sustainable, secure and competitive energy supply
Action type: Innovation actions
Countries involved: Greece,Portugal,Spain,France,Latvia,Italy,Luxembourg,Poland
Call: AI Testing and Experimentation Facility (TEF) for the energy sector – bringing technology to the market (HORIZON-CL5-2024-D3-01, 2024)
Start date: 2025-01-01
End date: 2027-12-31
Duration (months): 36.0
Total project cost (EUR): 5752248.5
EU funding (EUR): 4939644.46
Participants: 16
Project status: SIGNED

EnergyGuard aims to develop, kickstart and sustain an open, green and robust Testing Experimentation Facility operating under real-world conditions to empower innovators in bringing trustworthy AI products to the energy market in a cost-effective manner. It will integrate five significant European large-scale testing and experimentation facilities that cover the full energy value chain,supported by European’s greenest HPC infrastructure (Meluxina). This includes a digital twin (DT) of the Portuguese Transmission Network (RDN), the CEDER-CIEMAT Microgrid with its Distributed Energy Resources (DERs), the Hydrogen testing platforms at CEA LITEN, CARTIF, BER and CIEMAT, a high-fidelity local DT of Riga's multi-apartment residential buildings and the Antrodoco Renewable Energy Community. This includes a wide range of elements to cover diverse AI test needs,including wind power, photovoltaic systems, hydropower plant, AEM,PEM and SO eletrolysers, fuel cells, EV charging stations, electric and public buses and battery storage systems. The facilities will be accessible to EnergyGuard end-users through a set of properly configured Digital Twins (DTs) and curated assets, including data, models, inference APIs, services, and applications through a AI development Testing environment. It will enable easy seamless access to assets from the EU ecosystem including AIOD, Data Spaces, DIHs and other TEFs; Moreover, EnergyGuard facilitates users to validate their products with an Acceptance Environment and a common open AI risks database for a wide range of cybersecurity and trustworthy AI assessments. The TEF will serve as full infrastructure to support national AI regulatory sandbox initiatives and deliver 5 pilot cases for the private and public sector. EnergyGuard will build upon a long-term, self-sustainable business model driven by a new entity, incorporating market-ready features early in the design, such as a subscription/plan framework, billing, and professional support

Keywords: trustworthyai,testing and experimentation facility,energy,artificial intelligence,market,electricity,building,microgrid,digital twins,cybersecurity,der,storage,renewable energy community
Next generation toolbox for greener pharmaceuticals design & manufacturing towards reduced environmental impact (ENVIROMED)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Health
Theme: Health
Destination: Maintaining an innovative, sustainable and globally competitive health industry
Action type: Research and Innovation actions
Countries involved: Germany,Ireland,Greece,Denmark,Cyprus,Italy,Belgium,Austria,Switzerland,Lithuania,United Kingdom
Call: Green pharmaceuticals (HORIZON-HLTH-2021-IND-07, 2021)
Start date: 2022-06-01
End date: 2025-11-30
Duration (months): 42.0
Total project cost (EUR): 7518062.56
EU funding (EUR): 7518062.0
Participants: 19
Project status: SIGNED

Pharmaceuticals have undoubtably made our world a better place, ensuring longer and healthier lives. However, pharmaceuticals and their active metabolites are rapidly emerging environmental toxicants. It is thus critical that we fully understand, and mitigate where nec-essary, the environmental impact resulting from their production, use and disposal. In this direction, ENVIROMED addresses two aspects of the environmental impact of pharmaceuticals, a) impact of the processes in manufacturing the compound, and b) impact of the compound itself, during its lifecycle. The project narrows the knowledge gap when it comes to the effect of pharmaceutical compounds, and their derivatives, in the environment as it enables the better understanding the environmental impact of such compounds, throughout their lifecycle. It aims to offer (via extensive monitoring campaigns & scientific studies) information regarding occurrence of pharmaceuticals in the environment, their persistence, environmental fate, and toxicity (via in-vitro & in-vivo models) as well as application of in-silico methods to provide information about the basic risk management and fate prediction in the environment. Brief ideas about toxicity endpoints, available ecotoxicity databases, and expert systems employed for rapid toxicity predictions of ecotoxicity of pharmaceuticals will also be taken into account, in order to have a comprehensive approach to pharmaceuticals' Lifecycle Assessment (LCA). Moreover, the project aims at developing a set of technologies that enable greener and overall, more efficient pharmaceuticals production, which include: a) Green-by-design in-silico drug development; b) Novel sensing to allow reduction of rinsing chemicals and cycles; c) a robust Continuous Biomanufacturing line (CBM), which makes use of AI-enabled process optimisation and prediction, using data assimilation based on chemical sensing and energy disaggregation/monitoring. Training activities and a robust exploitation

Keywords: laser based sensors,green by design compounds,ecotoxicity studies,artificial intelligence,life cycle analysis
Machine learning-based prediction and evaluation of supercapacitor performance of transition metal carbide developed by using waste surgical masks during COVID-19 (MESTUM)

Framework: Horizon Europe — Widening Participation and Strengthening the European Research Area
Programme: Widening participation and spreading excellence
Theme: Attracting and mobilising the best talents
Destination: Attracting and mobilising the best talents
Action type: Marie Skłodowska-Curie actions
Panel: Chemistry
Countries involved: Czechia,Germany
Call: Fostering balanced brain circulation – ERA Fellowships (HORIZON-WIDERA-2022-TALENTS-04, 2022)
Start date: 2023-08-01
End date: 2025-07-31
Duration (months): 24.0
Total project cost (EUR): 166278.72
EU funding (EUR): 166278.72
Participants: 2
Project status: CLOSED

Fossil-free and renewable energy sources are a must for a sustainable society. Pushing the limitation of supercapacitors (SC) beyond their limits by producing metal carbides using as carbon source spent surgical masks, an immense side-product burden of COVID-19 pandemic, MESTUM project aims to tackle the worldwide increasing energy demand with highly efficient, stable, environmentally friendly and low-cost electrode materials (EM). The development of EM by transition metal carbides has attracted significant research attention thanks to its high-temperature stability, conductivity, easy operation in aqueous/organic electrolytes and insignificant electrolyte leaching. Easily accessible and abundant, manganese metal carbides, with multiple electron transfer ability, represent an excellent source possessing the key performance markers for SC: Ground-breaking pseudocapacitance and cyclic stability. By exploring the challenge of also generating a porous structure, MESTUM aims to expand an electrochemically active specific surface area and porosity of transition metal carbide and thus deliver a significant growth of specific capacity performance. During synthesis, the ground-breaking approach of turning the porous carbon from the surgical mask into the transition metal structure will orchestrate a brilliant coupling of increasing the surface area and reducing surgical mask waste. For machine learning prediction of specific capacity and cyclic stability, the two most critical performance-related features of SC, the materials properties (morphology, composition, surface area, etc.) and operational conditions (electrolytes, current density, potential window, etc.), are crucial. To tackle this issue, the project will set the optimized parameters to easily evaluate the prospects of developed materials in SC application. By a pioneering effort toward the significant advancement of SC technologies, the MESTUM project will address a fundamental need for clean energy sources.

Keywords: transition metal carbide,machine learning,surgical mask,supercapacitor