Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
iPROLEPSIS: PSORIATIC ARTHRITIS INFLAMMATION EXPLAINED THROUGH MULTI-SOURCE DATA ANALYSIS GUIDING A NOVEL PERSONALISED DIGITAL CARE ECOSYSTEM (iPROLEPSIS)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Health
Theme: Health
Destination: Staying Healthy in a rapidly changing society
Action type: Research and Innovation actions
Countries involved: Greece,Luxembourg,Belgium,Latvia,Israel,Portugal,United Kingdom,Netherlands,Germany
Call: Personalised blueprint of chronic inflammation in health-to-disease transition (HORIZON-HLTH-2022-STAYHLTH-02, 2022)
Start date: 2023-01-01
End date: 2026-12-31
Duration (months): 48.0
Total project cost (EUR): 6483091.25
EU funding (EUR): 6483091.25
Participants: 17
Project status: SIGNED

Psoriatic Arthritis (PsA) is a chronic, progressive, inflammatory disease affecting 1-2% of the general population, while manifesting in up to 30% of people with psoriasis (PsO). The transition from health to PsA is currently untraceable; diagnosis of early PsA is challenging even in PsO patients. Untimely diagnosis is common and contributes to early deterioration of quality of life, also increasing the burden of the multiple comorbidities associated with PsA. In this vein, iPROLEPSIS aspires to shed light upon the health-to-PsA transition with a comprehensive multiscale/multifactorial PsA model employing novel trustworthy AI-based analysis of multisource and heterogenous (i.a., in-depth health, environmental, genetic, behavioural) data, digital phenotyping of inflammatory symptoms with emphasis on tracking of motor manifestations using smart devices and wearables, novel optoacoustic imaging-based markers of PsA in the skin and joints, and investigation of the role of mast cells in the PsA transition, to identify key drivers of the disease and support personalized models for PsA risk/progression prediction and monitoring as well as associated inflammation detection and severity assessment. To ultimately advance PsA diagnosis and care, the models will be translated into a digital health ecosystem comprising dependable tools for supporting healthcare professionals in disease screening, monitoring and treatment via quantitative, explainable evidence, and empowering people with/at risk of PsA with tailored insights and preventive interventions based on actionable factors for educated health management. The project will steer its research and development efforts following a trustworthy framework for ethical, lawful, and robust AI, and a user-centered co-creation approach based on constant involvement of key stakeholders during the design, development, and testing of the digital health ecosystem, securing successful integration of the latter in the continuum of care.

Keywords: psoriatic arthritis,ai
Hay data analytics & fermentation control to increase dairy farm milk yield (HAYTECH AI)

Framework: Horizon Europe — Innovative Europe
Programme: The European Innovation Council (EIC)
Theme: EIC Accelerator
Destination: Open to supporting projects in all areas of science, technology,...
Action type: EIC actions
Countries involved: Finland
Call: EIC Accelerator Open (HORIZON-EIC-2022-ACCELERATOR-01, 2022)
Start date: 2023-02-01
End date: 2025-10-31
Duration (months): 33.0
Total project cost (EUR): 3571250.0
EU funding (EUR): 2499875.0
Participants: 1
Project status: SIGNED

Quanturi is an AgTech startup providing IoT solutions for monitoring bulk-stored organic materials. HAYTECH is a network of 1000s of probes that collect temperature & moisture readings from hay bales & transmit them to Quanturi Cloud. The analytical engine constantly monitors each bale’s accumulation of temperature data points over time as well as the environmental conditions, determining the nutritional value of hay at specific points in time. Having collected an extensive database of complex datasets incl. hay species, hay density, size of the bale, and past and current situation, we are now building a full HAYTECH AI stack that offers automated hay fermentation control and cow feed optimization to improve milk yields for dairy farmers. We are a team of 15 FTEs with customers in the EU, CA, the US and AU. We are aiming to introduce HAYTECH to the dairy industry in 2025. Further business scaling will see us reach annual revenues of €50M+.

Keywords: agricultural products,animal and dairy science,milk production,dairy farming,fermentation control,forage analysis,feed quality,data processing,machine learning,hay
MAGNONIC ARTIFICIAL NEURAL NETWORKS AND GATE ARRAYS (MANNGA_Hop_on)

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: Research and Innovation actions
Countries involved: Finland,Poland
Call: Hop on Facility (HORIZON-WIDERA-2023-ACCESS-06, 2023)
Duration (months): 18.0
Total project cost (EUR): 331375.0
EU funding (EUR): 331375.0
Participants: 2
Project status: SIGNED

The main objective of the MANNGA project, which is expected to be joinedWe seek to explore and challenge the limits of spin-based devices and their energy efficiency. This will be achieved by combining two inherently energy-efficient technology paradigms: (i) magnonics (using spin waves – low energy magnetic excitations – to process signals and data) and (ii) neuromorphic computing (using large-scale integrated systems and analog circuits to solve data-driven problems in a brain-like manner). We will use nanoscale chiral magnonic resonators as building blocks of artificial neural networks. The power of the networks will be demonstrated by creating magnonics versions of field programmable gate arrays, reservoir computers, and recurrent neural networks. The ultimate efficiency of the devices will be achieved by (a) maximising their magnetic nonlinearity; (b) using epitaxial yttrium iron garnet, which has the lowest known magnetic damping, for thin film magnonic media and resonators; and (c) using wireless delivery of power. Sensitive to the resonators’ micromagnetic states, such artificial neural networks will be conveniently programmable and trainable within existing paradigms of magnetic data storage. The latter includes magnetic random-access memory (MRAM), which is already compatible with CMOS, while compatibility with other technology paradigms of spintronics will also be sought, explored, and exploited. Thereby, the key ambition of our proposed very forwardlooking research programme is to develop and establish a novel, revolutionary class of energy-efficient spin-based components and devices for use in green high-tech data communication, processing, and storage technologies, thereby helping unlock the full potential of spintronics. We will seek dissemination of our developed and appropriately protected designs, processes, and technologies to interested European and international companies, thereby improving the competitiveness of the European high-tech industry.

Keywords: magnonics,spin waves,spintronics,machine learning,artificial neural networks
Particle emission prevention and impact: from real-world emissions of traffic to secondary PM of urban air (PAREMPI)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Climate, Energy and Mobility
Theme: Mobility
Destination: Clean and competitive solutions for all transport modes
Action type: Research and Innovation actions
Countries involved: Finland,Poland,Sweden,France,Czechia,Portugal,Italy
Call: Prevent smog episodes in Europe: Air quality impact of engine-emitted volatile, semi volatile and secondary particles (HORIZON-CL5-2022-D5-01, 2022)
Start date: 2023-01-01
End date: 2026-03-31
Duration (months): 39.0
Total project cost (EUR): 2996544.75
EU funding (EUR): 2996544.75
Participants: 9
Project status: SIGNED

Mobility is essential to our modern world to move people and goods between places and countries to serve needs relating to food, energy and all human necessities. However, mobility and its benefits do not come without harm: all transport sectors are substantial emitters of greenhouse gases and air pollutants. Furthermore, transport sectors, particularly aviation and maritime, are difficult to decarbonise by electrification, hence the combustion engines and turbines will remain in use for the foreseeable future. Energy efficiency increases and carbon-neutral fuels will reduce the climate impact of these technologies, but solutions are needed also to remove exhaust emissions completely, including those formed through secondary reactions in the atmosphere. Particulate matter (PM2.5) emissions are especially harmful, causing premature deaths and significant harm to humans and all global ecosystems. The PAREMPI project will reveal the contribution of the secondary aerosols (SecA) from transport sources to ambient PM2.5 levels via increased understanding of precursors, their atmospheric reactions and by a novel digital software (ePMI module) to be developed in the PAREMPI project. In combination with toxicity and health impact assessments, quantification of transport externalities will be improved. The consortium members are top-scientists in the research of the precursor emissions, SecA formation, and health impact of aerosols, many of them have focused in these topics all their careers and earlier collaboration assure seamless work. Consortium members have experts also on road, non-road, marine and aviation sectors, and on the emissions standards. Thus, the consortium is capable of formulating sound policy recommendations based on scientific evidence obtained. The PAREMPI results, efficiently disseminated, communicated and exploited, have the potential to significantly contribute to the goal of making transportation systems in Europe clean, secure, and efficient.

Keywords: exhaust emissions,particles,pm,pn,emissions,pn,precursor emissions,secondary aerosols,toxicity,health impact,external costs,modelling,policy recommendations,clean air,zero emission
Transforming R&I organisations and advancing the ERA through gender equality and inclusiveness in SEE (SEE-ERA)

Framework: Horizon Europe — Widening Participation and Strengthening the European Research Area
Programme: Reforming and enhancing the European R&I System
Theme: Reforming and enhancing the European R&I System
Destination: Reforming and enhancing the European R&I System
Action type: Coordination and support actions
Countries involved: Greece,United Kingdom,Denmark,Romania,Croatia,Albania
Call: Support to the implementation of inclusive gender equality plans (HORIZON-WIDERA-2024-ERA-01, 2024)
Start date: 2025-02-01
End date: 2028-01-31
Duration (months): 36.0
Total project cost (EUR): 998676.25
EU funding (EUR): 998676.25
Participants: 10
Project status: SIGNED

The SEE-ERA project aims at driving a structural change process through the sustainable transformation of Gender Equality Plans (GEPs) in five Research Performing Organisations (RPOs) and two Research Funding Organisation (RFO) operating in four different South East European (SEE) countries (Greece, Albania, Croatia and Romania) involving internal and external stakeholders since the beginning of the project.The project goal is to intensify efforts and enhance gender capacities for the implementation of sustainable GEPs, transforming RPOs/RFOs and their respective innovation ecosystems towards inclusive Gender Equality (GE) through the development and delivery of tailored and reflexive mentoring and training programmes. The project supports opening up the GE agenda beyond RPOs and RFOs aspiring to connect inclusive and intersectional GEPs with inclusive innovation. Thus, SEE-ERA contributes towards applying an inclusive approach towards forging new research careers, better integration of the gender dimension in research and innovation (R&I), knowledge valorisation and building stronger R&I communities and ecosystems in countries that often lag behind in terms of knowledge creation and innovation. SEE-ERA has a geographic focus on SEE countries but at the same time adopts a wider range by involving partners from Northern Europe providing a space for participating institutions to learn from each other and reflect together on how they can make their organisations more inclusive in an effective and sustainable fashion. The project ensures its successful implementation by adopting a holistic approach composed of capacity building support to develop and sustain internal gender+ knowledge, skills and expertise, mutual learning among partners, a participatory co-design of mentoring and training scheme and a thorough monitoring and developmental evaluation mechanism leading to durable institutional changes.

Keywords: inclusive,inclusiveness,intersectionality,gender,south east europe see,training and mentoring,capacity building,evaluation,structural and organisational transformation
Common European-scale Energy Artificial Intelligence Federated Testing and Experimentation Facility (EnerTEF)

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,Luxembourg,Portugal,Sweden,Italy,Netherlands,Slovenia,Germany,France,Spain
Call: AI Testing and Experimentation Facility (TEF) for the energy sector – bringing technology to the market (HORIZON-CL5-2024-D3-01, 2024)
Start date: 2024-11-01
End date: 2027-10-31
Duration (months): 36.0
Total project cost (EUR): 6687663.75
EU funding (EUR): 5526007.5
Participants: 25
Project status: SIGNED

EnerTEF responds to the soaring demand for reliable AI products in the evolving energy sector. It pioneers a Common Federated AI Testing and Experimentation Facility to bridge the gap between demand and limited availability of rigorously tested AI products. Aligned with European Commission initiatives, EnerTEF leverages foundational projects like the Common European Energy Data Space and AIoD. Through specialized 'AI Living Labs,' EnerTEF fosters collaboration between energy stakeholders and AI developers to create adaptive models that align with evolving energy systems. 'Bridging Centralized Access' ensures data privacy and security in federated testing, adhering to EU regulations and addressing ethical concerns. 'Seamless Federated Integration' tackles integrating AI technologies across domains, enhancing cross-sector collaboration. 'Long-term Sustainability and Go-to-Market Strategy' focuses on bringing AI innovations to market. In a nutshell, EnerTEF's primary goal is to establish a Common European-scale Energy AI Federated TEF, facilitating extensive adaptation, validation, and upscale of AI tools and services across the energy sector. The project's network of five primary nodes, complemented by three satellites, ensures a comprehensive approach to testing AI-based technologies in real-world operational environments. This setup contributes to the market uptake of trustworthy AI, ensuring their readiness, accessibility, and applicability to real-world scenarios.

Keywords: tef,ict,ai in energy,cee ai tef
Open Force Microscopy for Biology and Biomedicine (OpenFMLab)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Proof of concept grants
Action type: ERC actions
Countries involved: France
Call: ERC PROOF OF CONCEPT GRANTS (ERC-2024-POC, 2024)
Start date: 2024-09-01
End date: 2026-02-28
Duration (months): 18.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: SIGNED

The emergence and establishment of mechanobiology has led to a growing demand for the mechanical characterization of biological samples. Moreover, nanomechanical tests for diagnosis and prognosis of diseases are now being applied in clinical trials. Atomic force microscopy (AFM) is likely the most versatile, robust, and standardized tool for nanomechanical tests of biological systems. However, commercial AFM systems are expensive, not dedicated to mechanical tests, and difficult to use and customize. To solve these problems, we propose to develop and build an open-source force microscopy prototype: openFMLab. Built on the instrumentation developed during ERC Consolidator project MechaDynA, the openFMLab system will be affordable, customizable, user-friendly, and dedicated to mechanical tests in biology. Cost reduction will be assured by using open-source design, hardware and software and a function exclusively dedicated to mechanical measurements. The development of robust protocols and algorithms for calibration, data acquisition and data processing will provide ease-of-use. Finally, the system will be modular and adaptable to any optical microscope and experimental setup. During the openFMLab project, we will design, build, implement, and test the system in-house and with external end-users at biology and biomedical laboratories and industrial partners. We will explore cost reduction and create collaborations to add value to the system and define licensing and knowledge transfer and commercialization strategies. The final goal of openFMLab is to make AFM for nanomechanical testing a common tool in any laboratory.
Integrating Terrestrial and Aquatic Archaeozoological Studies: Venice and the northern Adriatic lagoons between Antiquity and the Early Middle Ages (InTer AquAS)

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: Social Sciences and Humanities
Countries involved: Italy,Spain
Call: MSCA Postdoctoral Fellowships 2021 (HORIZON-MSCA-2021-PF-01, 2021)
Start date: 2022-10-01
End date: 2024-11-30
Duration (months): 26.0
Total project cost (EUR): 172750.08
EU funding (EUR): 172750.08
Participants: 2
Project status: CLOSED

The InTer AquAS project aims to investigate human-animal-environment relationships at the Roman-early medieval transition along the north-western Adriatic coast; by highlighting elements of continuity and discontinuity, the project aims to contribute precious knowledge to the archaeology and history of this transitional period. The project will analyse archaeozoological assemblages from three study-regions: the Venetian Lagoon, the Marano-Grado Lagoon, and the coast of Romagna; these regions are characterised by the presence of wetlands, with lagoons, rivers and canals influencing the nature of human settlement and activities. The archaeology of the Late Antiquity and Middle Ages in the north-western Adriatic represents a very active field of research; current archaeozoological analyses, however, are site-specific, while large-scale reviews touch these regions only lightly and marginally, without focussing on their peculiar environmental contexts. The chronological and spatial comparisons proposed by the InTer AquAS project, the combination of terrestrial and aquatic faunal studies, and the functional implementation of biometrical and stable isotope analyses, will provide a holistic approach taking into account the complex, changing relationships between humans, other animals, and the environment. My experience in archaeozoology, and in particular my familiarity with biometry and the Roman-early medieval transition in Europe, make me the ideal candidate to conduct a project such as InTer AquAS. Ca’ Foscari University of Venice is a leader in the study of the Late Antiquity and Middle Ages in Italy, and the project will ultimately fill the existing gap in large-scale and methodologically targeted faunal analyses. In addition to informing on key aspects of past economies and environments, this study will boost my knowledge on the archaeology and history of the periods considered, and substantially advance methodological skills required to optimise my career prospects.

Keywords: zooarchaeology,northern adriatic,roman period,early middle ages,biometry,stable isotope analysis,wetlands,human animal environment relationships
Photoelectrosynthetic processes in continuous-flow under concentrated sunlight: combining efficiency with selectivity (SunFlower)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Physical and Analytical Chemical Sciences
Panel description: Analytical chemistry, chemical theory, physical chemistry/chemical physics
Countries involved: Hungary
Call: ERC CONSOLIDATOR GRANTS (ERC-2021-COG, 2021)
Start date: 2022-06-01
End date: 2027-05-31
Duration (months): 60.0
Total project cost (EUR): 1999750.0
EU funding (EUR): 1999750.0
Participants: 1
Project status: SIGNED

To be the first CO2-neutral continent by 2050, Europe needs to develop and implement disruptive new technologies, based on scientific breakthroughs. In this regard, utilization of CO2 and organic waste as feedstock to generate valuable products will play a key role in turning the chemical industry on a more sustainable, circular path. In the SunFlower project, we are going to demonstrate that two high-value processes (CO2 or CO reduction and glycerol oxidation will be studied first) can be synergistically coupled to produce chemicals (such as ethylene and lactic acid) and fuels, using novel photoelectrode assemblies (both photocathodes and photoanodes), original photoelectrochemical (PEC) device architectures, and automated processes. The SunFlower project is based on the following three hypotheses: 1. Proper engineering of continuous-flow PEC cells operating under concentrated sunlight will allow current densities similar to the electrochemical (EC) methods. 2. One semiconductor alone can supply the necessary energy input for bias-free operation of PEC cells, while generating two high-value products. 3. PEC methods can provide superior selectivity compared to their EC counterparts, even at high current density operation (as the current density and potential can be decoupled). To validate our hypotheses, we are going to use for the first time: • The pairing of two high-value generating redox processes (none of them being H2 or O2 evolution). • Concentrated sunlight (which has only been used for water-splitting so far). • Custom-designed and developed PEC cells, elaborating on the photo-gas diffusion electrode concept. • Machine learning, based on the broad dataset collected by the sensors built in the PEC system, optimizing the performance at a system level. The proposed combination of these novel approaches will be of groundbreaking nature, therefore, it opens a whole new arena of solar energy conversion.

Keywords: solar energy conversion,photoelectrochemistry,circular economy