Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
300mm Silicon Technology for Applications Relying on Light with Photonics Devices (STARLight)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Digital, Industry and Space
Theme: Public-private partnerships
Destination: Chips for Europe Initiative
Action type: Innovation actions
Partnership: CHIPS (JU-JTI)
Countries involved: France,Italy,Germany,Israel,Greece,Ireland,United Kingdom,Belgium,Spain,Sweden,Switzerland
Call: Topic 1 Global call according to SRIA 2024 (HORIZON-JU-Chips-2024-1-IA, 2024)
Start date: 2025-06-01
End date: 2028-05-31
Duration (months): 36.0
Total project cost (EUR): 101369269.0
EU funding (EUR): 25481961.44
Participants: 30
Project status: SIGNED

STARLight is a major opportunity to establish Europe at the forefront of 300mm Silicon Photonics (SiPho) innovation and thus “Solidify the European photonics ecosystem and accelerate its industrial deployment”. The main STARLight vision is to contribute to European sovereignty relying on the achievement of an efficient and resilient ecosystem and a sustainable society by driving excellence and innovation in the implementation of 300mm silicon photonics manufacturing capacity in Europe (STMicroelectronics, France). The main STARLight goals are: -To offer Europe a realistic solution ensuring its independency on the sourcing of SiPho and other needed optical module compounds (EIC, laser), based on a true European end-to-end value chain, tailored to the global market. -To give Europe the opportunity to move forward with industrial and academic players by joining in the risk-taking necessary for the growth dynamics of SiPho optical modules in Europe, addressing applications such as Data Centers including Hyperscalers, Artificial Intelligence, Telecom, Automotive Lidar, Inter-Satellites communication. -To expand the European photonics technology platform family in terms of accessibility, upscaling and high yield, packaging assembly and providing modelling tools to converge on a mainstream simulation flow. -To be ready for future applications with 200 GBaud (400 Gbps PAM4/lane) by leveraging leading-edge SiPho technology innovations using the integration of heterogeneous non-silicon materials. -To demonstrate the benefit of SiPho optical modules sustainability, providing a better energy-saving, reliability, limited usage of copper and aluminium materials, and reducing the usage of polluting material and water. The STARLight project makes it possible to promote a concept of collaboration, which is based on an ecosystem bringing together the various actors of the supply chain ranging from the technologies, the circuits providers to the manufacturer of modules and systems.

Keywords: sovereign and green silicon photonics integrated lasers semiconductor optical amplifiers 300mm facilities hyperscalers telecom automotive lidar inter satellites communication accelerated ai
Engineered plant-derived extracellular vesicles tagged with magnetic nanoparticles as novel antitumoral agents (EMERGE)

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: Chemistry
Countries involved: Spain,Italy
Call: MSCA Postdoctoral Fellowships 2024 (HORIZON-MSCA-2024-PF-01, 2024)
Start date: 2025-07-01
End date: 2027-06-30
Duration (months): 24.0
Total project cost (EUR): 209914.56
EU funding (EUR): 209914.56
Participants: 2
Project status: SIGNED

In recent decades, extracellular vesicles (EVs) have emerged as promising therapeutics in biomedicine due to their crucial role in intercellular communication and their capacity to deliver biomolecules such as proteins, lipids, and RNAs to recipient cells. Despite their potential, EVs derived from mammalian sources face several challenges, including safety concerns, high production costs, and inconsistent reproducibility. Plant-derived EVs, however, present a safer and more effective alternative, thanks to their non-toxic nature, lack of immunogenicity, and unique molecular biocargo. The EMERGE project aims to create innovative hybrid nanomaterials for cancer treatment by combining plant-EVs with magnetic nanoparticles (MNPs). This novel approach not only exploit the inherent safety and therapeutic potential of plant-EVs but also addresses production challenges by employing plant cell cultures (PCCs) as biofactories for EV production. MNPs will serve a dual purpose: they will facilitate the selective purification of EVs from PCC-conditioned media and enhance their anticancer effects through magnetic hyperthermia (MHT). The project will focus on refining EV purification and functionalization, optimizing the loading of anticancer agents, and assessing the effectiveness of MNP@eEVs in both traditional 2D cancer cell models and advanced 3D patient-derived tumor organoids (tumoroids). Tumoroids, as advanced models derived from patient samples, offer a cutting-edge approach to personalized medicine, providing more precise testing environments for therapeutic strategies. By integrating plant-derived EVs with MNPs and utilizing MHT, EMERGE aims to push the boundaries of drug delivery systems and enhance cancer treatment outcomes, marking a significant advancement in the field of nanomedicine.

Keywords: plants,extracellular vesicles,tumoroids,nanomedicine,magnetic nanoparticles
Energy-efficient AI-ready Data Spaces (Green.Dat.AI)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Digital, Industry and Space
Theme: Digital
Destination: World-leading Data and Computing Technologies
Action type: Innovation actions
Partnership: AI-DATA-ROBOTICS (co-programmed)
Countries involved: Greece,Italy,Spain,Slovenia,Germany,Portugal,Luxembourg,Switzerland,Ireland
Call: Technologies for data management (AI, Data and Robotics Partnership) (IA) (HORIZON-CL4-2021-DATA-01, 2021)
Start date: 2023-01-01
End date: 2025-12-31
Duration (months): 36.0
Total project cost (EUR): 6658970.09
EU funding (EUR): 5507269.0
Participants: 23
Project status: SIGNED

GREEN.DAT.AI aims to channel the potential of AI towards the goals of the European Green Deal, by developing novel Energy-Efficient Large-Scale Data Analytics Services, ready-to-use in industrial AI-based systems, while reducing the environmental impact of data management processes. GREEN.DAT.AI will demonstrate the efficiencies of the new analytics services in four industries (Smart Energy, Smart Agriculture/Agri-food, Smart Mobility, Smart Banking) and six different application scenarios, leveraging the use of European Data Spaces. The ambition is to exploit mature (TRL5 or higher) solutions already developed in recent H2020 projects and deliver an efficient, massively distributed, open-source, green, AI/FL - ready platform, and a validated go-to-market TRL7/8 Toolbox for AI-ready Data Spaces. The services will cover AI-enabled data enrichment, Incentive mechanisms for Data Sharing, Synthetic Data Generation, Large-scale learning at the Edge/Fog, Federated & Auto ML at the edge/fog, Explainable AI/Feature Learning with Privacy Preservation, Federated & Automatic Transfer Learning, Adaptive FL for Digital Twin Applications, Automated IoT event-based change detection/forecasting. The GREEN.DAT.AI Consortium consists of a multidisciplinary group of 17 partners from 10 different countries (and one associated party), well balanced in terms of expertise. The vast majority of partners already have key roles in a number of projects funded under the Big Data PPP (ICT-16-2017) topic, namely BigDataStack, CLASS, Track & Know, and I-BiDaaS and are serving as active members of the BDVA/DAIRO Association, FIWARE, AIOTI, and ETSI. In addition, partners come from a variety of sectors, such as banking, mobility, energy, and agriculture, constituting a representative workforce of their respective domains, which will contribute to industry adoption and stimulate uptake in other sectors as well.

Keywords: digital twins,energy efficiency,federated learning,explainable ai,automl,transfer learning
Fast yet accurate routine rational design of novel enzymes (FASTEN)

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: Spain
Call: ERC CONSOLIDATOR GRANTS (ERC-2022-COG, 2022)
Start date: 2023-10-01
End date: 2028-09-30
Duration (months): 60.0
Total project cost (EUR): 1996250.0
EU funding (EUR): 1996250.0
Participants: 1
Project status: SIGNED

Life could not be sustained without the presence of enzymes, which are responsible for accelerating the chemical reactions in a biologically compatible timescale. Enzymes present other advantageous features such as high specificity and selectivity, plus they operate under very mild biological conditions. Inspired by these extraordinary characteristics, many scientists wondered about the possibility of designing new enzymes for industrially-relevant targets. Unfortunately, none of the current enzyme design strategies is able to rapidly design tailor-made enzymes at a reduced cost. This is limiting the general routine application of enzyme catalysis in industry, and thus the chemical manufacturing competitiveness. The goal of this project is to develop a fast yet accurate computational enzyme design approach for allowing the routine design of highly efficient enzymes. FASTEN combines computational chemistry, deep learning, graph theory, and computational geometry for controlling the complexity of enzyme catalysis in a new computational protocol that will capture the chemical steps and conformational changes that take place along the catalytic itinerary. Active site and distal activity-enhancing mutations are predicted based on correlation and co-evolutionary-based guidelines, and the catalytic potential of the new designs is estimated by means of geometry-based oracles. This new computational approach will be validated with the design of enzymes presenting complex conformational dynamics and multi-step mechanisms. The experimental evaluation of many of the designs will finally reveal the potential of this new approach for the fast routinely design of industrially-relevant enzymes. FASTEN has the potential of making the routine design of enzymes possible, thus improving our current lives and leading to a more sustainable world for our generations.

Keywords: computational enzyme design,molecular dynamics simulations,alphafold2,graph theory,quantum mechanics,computational geometry
Geothermal Exploration and Optimization through Forward Modeling and Resource Development (GO-Forward)

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: Research and Innovation actions
Countries involved: Germany,Denmark,Netherlands,Austria,Spain,Switzerland,United Kingdom,Italy
Call: Advanced exploration technologies for geothermal resources in a wide range of geological settings (HORIZON-CL5-2023-D3-02, 2023)
Start date: 2024-09-01
End date: 2028-08-31
Duration (months): 48.0
Total project cost (EUR): 4263623.75
EU funding (EUR): 4263623.75
Participants: 12
Project status: SIGNED

GO-Forward aims to develop a novel methodological approach to make more accurate pre-drilling predictions of geothermal reservoir properties and thus reduce the mining risk. Key to the GO-Forward approach is to simulate geological processes for pre-drill assessment of reservoir structure and properties, calibrated to geological or geophysical data, rather than extrapolating the properties from those data with geostatistical methods. To this end, GO-Forward focuses on extending and further developing, testing and demonstrating the added value of forward modelling methods originally developed for hydrocarbon exploration, including stratigraphic forward modelling (SFM), diagenesis forward modelling (DFM) and fracture network forward modelling (FFM), to be used for exploration in different geothermal settings of high relevance for Europe. First, the developed approaches will be tested and calibrated in areas with abundant subsurface information and production data, to prove conceptually the applicability of the methods and reproducibility of the results, to optimise and de-risk geothermal exploration. Calibrated model approaches are subsequently applied in areas with limited data availability to demonstrate their capability to increase pre-drill Probability of Success (POS). To support the workflow and further reduce exploration costs, GO-Forward advances ML-based and computational methods to enhance (existing) (sub)surface information for calibration, uncertainty quantification and data assimilation, and (upscaling) routines for flow simulation, DNSH, and techno-economic performance assessment for POS and Value of Information (VOI). In addition, GO-Forward addresses public awareness of geothermal developments already at the early stages of exploration. By including novel approaches to citizen engagement and stakeholder dialogue, we aim to increase the societal readiness level of geothermal exploration as the first step of geothermal developments.

Keywords: geothermal energy,geology,reservoir characterization,uncertainty reduction
Framework Partnership Agreement for open testing and experimentation for quantum technologies (Qu-Test)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Digital, Industry and Space
Theme: Digital
Destination: Digital & Emerging Technologies for Competitiveness and Fit for the Green Deal
Action type: Framework Partnership Agreement
Countries involved: Netherlands,Finland,Belgium,Germany,Austria,France,Italy,Czechia,Denmark,Spain
Call: Framework Partnership Agreements for open testing and experimentation and for pilot production capabilities for quantum technologies (FPA) (HORIZON-CL4-2021-DIGITAL-EMERGING-02, 2021)
Start date: 2023-03-01
End date: 2027-02-28
Duration (months): 48.0
Total project cost (EUR): 0.0
EU funding (EUR): 0.0
Participants: 31
Project status: SIGNED

Qu-Test is a partnership of European testbeds for quantum technology, which is composed of distributed infrastructures with globally unique equipment and competencies across Europe. The goal of the partnership is to provide European industry with the necessary support in terms of infrastructure and know-how to move faster to the market and create a robust supply chain for the quantum technology market. The partnership is aligned along three testbeds: quantum computing, quantum communication, quantum sensing. In more detail, the Quantum Computing Testbed will measure, characterise and validate cryogenic quantum devices, cryogenic qubits such as superconducting and semiconducting qubits, photonics qubits and ion traps provided by European industry, with an increasing service maturity and targeting larger quantum processors during the course of the FPA. The Quantum Communication Testbed will characterise devices for Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) and provide design and prototyping services to support innovation in the supply chain of quantum communication technologies. Finally, the Quantum Sensing Testbed will benchmark sensing and metrology instruments provided by industry and use a large suite of quantum sensors (clocks, gravimeters, magnetometers, imagers) to validate industrial use cases aiming at generating new business cases for quantum sensing and metrology devices. The three testbeds will be coordinated by a Single Entry Point (SEP) that will receive the requests of industry and direct them efficiently to the right testbed infrastructure. With additional services of IPR support, business coaching and innovation management, Qu-Test supports the European quantum industry with a holistic one-stop-shop to move the full ecosystem forward.

Keywords: quantum computing,quantum communication,quantum sensing,qubits,testing,test infrastructure
Optimal Planning with Tools that Integrate Green Regenerative Environments in Equitable Neighborhoods (OPTIGREEN)

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: Environment and Geosciences
Countries involved: Spain,Canada
Call: MSCA Postdoctoral Fellowships 2024 (HORIZON-MSCA-2024-PF-01, 2024)
Start date: 2025-05-01
End date: 2028-04-30
Duration (months): 36.0
Total project cost (EUR): 341898.72
EU funding (EUR): 341898.72
Participants: 3
Project status: SIGNED

Air pollution presents a significant public health challenge, contributing to approximately 9 million premature deaths globally each year, including 400,000 in Europe. Green infrastructure (GI)—encompassing urban trees, green roofs, and green walls-offers an innovative solution to mitigate the impacts of urban pollution while enhancing public health and quality of life. These approaches are not only cost-effective but also play a crucial role in promoting sustainability. OPTIGREEN seeks to develop a comprehensive methodology for designing, planning, and managing green infrastructure in European cities, with Pamplona serving as the pilot case. This methodology will determine optimal GI configurations to effectively reduce concentrations of harmful air pollutants, such as fine particulate matter (PM) and nitrogen dioxide (NO2), thereby improving respiratory and cardiovascular health. A key output of the project will be the development of an interactive geographic viewer, which will serve as a practical and versatile tool for dynamically visualizing and analyzing different intervention scenarios within the city. The project’s impact will be substantial, combining scientific innovation through the use of advanced computational fluid dynamics models with a practical framework that empowers urban planners and policymakers to make informed decisions for improving urban sustainability. OPTIGREEN will address critical challenges in green infrastructure management, including adapting designs to specific local conditions and efficiently implementing them in complex urban environments. By directly contributing to Sustainable Development Goals, OPTIGREEN will integrate science, technology, and urban planning to transform how cities tackle air pollution and its health impacts. With its interdisciplinary approach, the project will positively impact Pamplona and serve as a replicable model for other European cities, promoting healthier, more sustainable urban environments.

Keywords: efficient gi scenarios,computional fluid dynamics cfd,pollution and health impact indices,interdisciplinary team,interactive wiewer,advanced data management platfform,nature based solutions
New players in the regulation of DNA replication fork speed (META-CYCLE)

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: Denmark
Call: MSCA Postdoctoral Fellowships 2022 (HORIZON-MSCA-2022-PF-01, 2022)
Start date: 2024-01-01
End date: 2025-12-31
Duration (months): 24.0
Total project cost (EUR): 230774.4
EU funding (EUR): 230774.4
Participants: 1
Project status: SIGNED

Faithful eucaryotic cell division requires spatio-temporal orchestration of multiple sequential events. Among the crucial steps to providing the daughter cells with identical set of chromosomes is the DNA replication. During this phase, cells coordinate the speed of DNA synthesis with the length of the cell cycle to ensure genome integrity. To do so, growth factors and metabolic signals are integrated primarily by D-type Cyclins. Indeed. their deregulation can directly lead to some of the hallmarks of cancer by causing proliferation that is independent of normal extracellular cues. We previously demonstrated that aberrant accumulation of Cyclin D1 results in a faster cell cycle, with uncontrolled speed of DNA replication fork progression and genome instability (Maiani & Milletti et al., 2021). Despite, frequently altered in many tumors (Musgrove et al., 2011), a unifying theory that clarify how Cyclin D1 promote cancer transformation is still lacking. In the lab of Prof. Jiri Bartek, it was previously shown that PARP1 inhibition increases replication fork speed (Maya-Mendoza et al., 2018). PARP1i uncouples the leading and lagging DNA synthesis resulting in fast fork speed and genome instability. However, it is currently unknown whether the aberrant accumulation of Cyclin D1 has similar effect on DNA synthesis as PARPi. Furthermore, it is also undetermined whether aberrant levels of Cyclin D1 could trigger metabolic changes that accelerate the speed of DNA synthesis. Taking advantage of our previous observations, we aim with this proposal to: i) identify metabolic signatures that can predict dis-regulated DNA synthesis in response to cell cycle alterations; ii) define the molecular mechanism of how cyclin D1 accumulation induces accelerated fork speed; iii) identify new metabolic genes involved in the control of S phase progression and the speed of DNA synthesis by CRISPR-Cas9 screening technology; iv) suggest druggable targets that could be used in cancer therapy.

Keywords: cell cycle regulation in cancer