Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Biocatalytic C-C bond formation by repurposing transaminases (CCBioCat)

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: Netherlands
Call: MSCA Postdoctoral Fellowships 2023 (HORIZON-MSCA-2023-PF-01, 2023)
Start date: 2024-10-01
End date: 2026-09-30
Duration (months): 24.0
Total project cost (EUR): 187624.32
EU funding (EUR): 187624.32
Participants: 1
Project status: SIGNED

Carbon-carbon (C-C) bond formation is one of the most challenging synthetic reactions in chemistry. Current approaches require expensive and toxic transition metals with complex ligands that have limited selectivity. Developing biocatalytic sustainable methods for the controlled formation of C-C bonds with stereospecificity in high yields has the potential to advance organic chemistry and provide access to new molecules. Although several enzymes are capable of catalyzing C-C bonds, few biocatalytic approaches have been used in organic synthesis, with aldolases being the most used enzyme group. PLP-dependent transaminases (TA) are the largest and most versatile group, and used at industrial scale, but have never shown C-C bond formation activity. In this project, I will be developing biocatalytic approaches for C-C bond formation by exploring this new functionality of these versatile enzymes. Thus, I will explore the catalytic versatility of TAs through protein engineering to allow C-C bond formation. Protein immobilization of engineered TAs will support their biocatalytic application in preparative C-C bond formation reactions. This will expand the biocatalytic toolbox available for synthesis, and improve sustainability. This project will be carried in the Biocatalysis section at TU Delft, where I will receive training in protein engineering, while using my experience in immobilization, and co-supervise students. This project will give me the opportunity to further develop the research and supervision skills to become a future group leader.

Keywords: biocatalysis,transaminases,carbon carbon bond formation,enzyme immobilization,protein engineering
DEVELOPMENT OF OPERANDO TECHNIQUES AND MULTISCALE MODELLING TO FACE THE ZERO-EXCESS SOLID-STATE BATTERY CHALLENGE (OPERA)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Climate, Energy and Mobility
Theme: Energy
Destination: Cross-sectoral solutions for the climate transition
Action type: Research and Innovation actions
Partnership: Batteries (co-programmed)
Countries involved: Spain,Norway,Austria,Germany,Slovakia,United Kingdom,France
Call: Interface and electron monitoring for the engineering of new and emerging battery technologies (Batteries Partnership) (HORIZON-CL5-2022-D2-01, 2022)
Start date: 2023-06-01
End date: 2026-05-31
Duration (months): 36.0
Total project cost (EUR): 4416396.75
EU funding (EUR): 4416396.75
Participants: 12
Project status: SIGNED

Green, high-performing and safe batteries based on abundant materials are a key element in the transition to a carbon-neutral future. However, to accelerate their development, a deep understanding of the complex electro-chemo-mechanical processes within the battery is required, which is only accessible through advanced experimental and computational methods. Zero-excess solid-state batteries, where the anode is formed in situ, have emerged as a promising new generation of environmentally friendly batteries with high energy density, improved safety and higher cost-efficiency, but only after solutions for non-uniform anode formation were found. In OPERA, seven leading research institutions, two synchrotron radiation facilities, a small-medium sized enterprise and a large technological company, all from complementary research fields such as batteries, surface and material science, and multiscale modelling, propose a unique strategy to face the current challenges of this technology. OPERA relies on the development of novel operando experimental techniques at the ESRF, ALBA and DESY synchrotrons and at the lab-scale, providing complementary information on multiaxial stress fields, chemical composition, nucleation and growth kinetics, structural defect formation and degradation of well-defined model cells with a resolution down to the atomic scale. The new insights and collected multiparameter data will be incorporated into a novel multiscale modelling approach supported by machine learning algorithms. This will ultimately lead to a conceptual understanding of the in-situ anode formation and, based on this, innovative improvement approaches to enable this type of energy storage technology, which will be an important step towards increasing the global competitiveness, resilience and independence of the EU.

Keywords: operando synchrotron techniques,multiscale modelling,machine learning,zero excess solid state battery,battery interface,nucleation and growth kinetics,electro chemo mechanical properties
The first multi-messenger detection of a supermassive black hole binary (MMMonsters)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Universe Sciences
Panel description: Astro -physics/ -chemistry/ -biology; solar system; planetary systems; stellar, galactic and extragalactic astronomy; cosmology; space sciences; astronomical instrumentation and data
Countries involved: Greece
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): 1711750.0
EU funding (EUR): 1711750.0
Participants: 1
Project status: SIGNED

Supermassive black hole binaries (SMBHBs) are a natural outcome of galaxy mergers. They can be identified as quasars with periodic variability in time-domain surveys, or from strong gravitational waves (GWs) by Pulsar Timing Arrays (PTAs), making them exceptional multi-messenger sources. Despite their expected ubiquity, they have remained undetected, but upcoming data of unprecedented quantity and quality from the Legacy Survey of Space and Time (LSST) and increasingly more sensitive PTAs will bring these multi-messenger monsters (MMMonsters) within reach. MMMonsters at the intersection of astronomy and data science is an ambitious program to deliver the first multi-messenger detection of a SMBHB. Leveraging existing time-domain data, we will improve SMBHB searches advancing our understanding of binary signals and quasar noise. For the first time, we will search for more complex and likely more common non-sinusoidal periodicity. Using the LSST Data Previews, I will develop novel machine learning tools to capitalize on the LSST dataset. With advance preparation, MMMonsters will be poised to reliably detect binaries in the first few LSST data releases, making the project extremely timely and impactful. On the GW side, I will pave the way for the first PTA detection of an individually resolvable binary. I will build novel PTA detection pipelines that directly incorporate EM data, from a catalog of 20 million galaxies (which we will compile) including all the potential binary hosts. My approach will accelerate the first detection of a SMBHB and allow the subsequent identification of the binary host galaxy. I will forge the ultimate boost in binary detectability through the joint analysis of time- domain and PTA data in a multi-messenger data stream. MMMonsters will establish strong EU leadership in time-domain astronomy and GW physics through groundbreaking results and by training early career scientists in the emerging field of multi-messenger astrophysics.

Keywords: supermassive black hole binaries,multi messenger,pulsar timing arrays,time domain astronomy,lsst
Learning a Universal Similarity Function (LUSt)

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: Information Science and Engineering
Countries involved: Czechia,Netherlands
Call: MSCA Postdoctoral Fellowships 2023 (HORIZON-MSCA-2023-PF-01, 2023)
Start date: 2025-01-01
End date: 2026-12-31
Duration (months): 24.0
Total project cost (EUR): 150438.72
EU funding (EUR): 150438.72
Participants: 2
Project status: SIGNED

Multimedia content is indispensable in our society, necessitating effective content management. A critical aspect of this is assessing the similarity between two multimedia items like images, videos, and documents. LUSt's mission is to pioneer a universal similarity function capable of precisely measuring similarity across a broad spectrum of multimedia domains and tasks. Diverging from traditional problem-specific approaches prevalent in current literature, LUSt adopts a novel strategy. LUSt plans to break down multimedia items into their constituent parts, including image regions, video frames, and text sentences. Subsequently, a foundational model will be trained on input data comprising part similarities across various multimedia items. This strategic choice yields a universal input space with multiple advantages. Firstly, it promotes seamless collaboration across different domains and tasks, facilitating joint training and mutual enhancement among tasks, which will be further enriched through multi-task learning techniques. Secondly, it streamlines the integration of synthetic data during training, a key ingredient for large-scale training of a foundational model. The model architecture is grounded in transformer-based deep learning modules and will be fortified by pioneering positional encodings rooted in kernel methods. These positional encodings empower us to effectively manage the differing part topologies encountered across diverse domains -- a formidable challenge in itself. The work program commences by focusing on a single domain and task but is thoughtfully designed for extensibility. The ultimate goal is creating a foundational model capable of accommodating all modalities -- visual, audio, text -- and supporting a broad range of similarity types, including uni-modal, cross-modal, and multi-modal scenarios. LUSt's commitment to universality will be thoroughly validated through comprehensive benchmarking, spanning numerous tasks and domains.

Keywords: similarity learning,multimodal learning,foundational model,positional encoding,self supervised learning,deep learning,computer vision
Developing climate-resilient and eco-sustainable railway earthwork by mitigating geohazards while balancing biodiversity (RESOLVE)

Framework: Horizon Europe — Excellent Science
Programme: Marie Skłodowska-Curie Actions (MSCA)
Theme: Marie Skłodowska-Curie
Destination: Staff Exchanges
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: Italy,Singapore,China,France,Sweden,Portugal,United Kingdom,Thailand,Japan,Norway
Call: MSCA Staff Exchanges 2023 (HORIZON-MSCA-2023-SE-01, 2023)
Start date: 2025-03-01
End date: 2029-02-28
Duration (months): 48.0
Total project cost (EUR): 1054057.14
EU funding (EUR): 1030400.0
Participants: 13
Project status: SIGNED

The resilient and sustainable development of railway networks is fundamental for economic and social progress, especially under the current climate. To achieve this, we must embrace new innovative solutions while replicating best practices that benefit nature. Alongside increasing the infrastructure resilience and safety, we need transformative changes in railway earthwork asset development that go beyond prioritising decarbonisation and digitalisation and seek to restore nature, safeguard biodiversity and produce positive outcomes for human societies. Leveraging cutting-edge technologies like AI, earth observation, statistical analysis, advanced numerical modelling, laboratory tests and sensor technologies, RESOLVE seeks to integrate conventional engineering practices with life sciences, empowered by data science. Furthermore, RESOLVE will pioneer synergistic solutions that tackle climate change adaptation in railway earthwork assets while creating opportunities for nature restoration. This attempt involves developing nature-based solutions (NBS) and environmentally friendly construction materials, with the additional aim of fostering biodiversity restoration beyond ground reinforcement. Highly skilled researchers and practitioners, capable of dealing with such problems are scarce and in high demand by academia and industry. Thus, formed with 11 world leading research organisations and 3 companies across Europe and internationally with expertise and facilities in these areas, RESOLVE aims to ensure comprehensive, robust and implementable solutions are obtained for railway network climate resilience built and eco-sustainable development. The network is carefully designed to enables research and innovative staff exchange across all aspects. RESOLVE secondees will enjoy a highly integrated, interdisciplinary and intersectoral staff exchange, sharing know-how and skills development environment through the planned secondments, networkwide events and local trainings.

Keywords: biodiversity eco sustainable geohazards
AI4SoilHealth: Accelerating collection and use of soil health information using AI technology to support the Soil Deal for Europe and EU Soil Observatory (AI4SoilHealth)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Mission
Theme: Soil health and food
Destination: Soil health and food
Action type: Research and Innovation actions
Countries involved: Denmark,Croatia,Netherlands,Finland,Austria,Greece,Germany,Spain,Italy,France,Switzerland,United Kingdom,Hungary,Sweden
Call: Validating and further developing indicators for soil health and functions (HORIZON-MISS-2021-SOIL-02, 2021)
Start date: 2023-01-01
End date: 2026-12-31
Duration (months): 48.0
Total project cost (EUR): 9960358.5
EU funding (EUR): 9960357.5
Participants: 30
Project status: SIGNED

The objective of AI4SoilHealth is to co-design, create and maintain an open access European-wide digital infrastructure, compiled using state-of-the-art Artificial Intelligence (AI) methods combined with new and deep soil health understanding and measures. The AI-based data infrastructure functions as a Digital Twin to the real-World biophysical system, forming a Soil Digital Twin. This can be used for assessing and continuously monitoring Soil Health metrics by land use and/or management parcel, supporting the Commission’s objective of transitioning towards healthy soils by 2030. The project is divided into seven (7) work-packages including: (WP2) Policy and stakeholder engagement - networking and synchronising with EU and national programmes, (WP3) Soil health methodology and standards - developing/testing methodology to be used by WPs 4-6, (WP4) Soil health in-situ monitoring tools and data - developing field and laboratory solutions for Observations & Measurements, (WP5) Harmonised EU-wide soil monitoring services - developing the final suite of tools, data and services, (WP6) Multi-actor engagement pilots - organizing field-works and collect users' feedback, (WP7) Soil literacy, capacity building and communication - organizing public campaigns and producing educational materials. Key deliverables include: 1) Coherent Soil Health Index methodology, 2) Rapid Soil Health Assessment Toolbox, 3) AI4SoilHealth Data Cube for Europe, 4) Soil-Health-Soil-Degradation-Monitor, and 5) AI4SoilHealth API and Mobile phone App. Produced tools will be exposed to target-users (including farmer associations in >10 countries), so their feedback is used to improve design/functionality. Produced high-resolution pan-European datasets will be distributed under an Open Data license, allowing easy access by development communities. AI4SoilHealth will provide an effective Soil Health Index certification system to support landowners and policy makers under the new Green Deal for Europe.
Twinning for Optimized Decision Intelligence in Data-Intensive Environments (TwinODIS)

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
Panel: Information Science and Engineering
Countries involved: Greece,Netherlands,France
Call: Twinning Bottom-Up (HORIZON-WIDERA-2023-ACCESS-02, 2023)
Start date: 2024-10-01
End date: 2027-09-30
Duration (months): 36.0
Total project cost (EUR): 1488125.0
EU funding (EUR): 1488125.0
Participants: 3
Project status: SIGNED

Dynamic decision-making in large-scale uncertain systems poses significant challenges in modern data-rich operational environments. Traditional decision support systems struggle to manage vast and complex datasets in real time, while handling multiple conflicting objectives of high-dimensional problems and ensuring fairness and transparency in decision-making. A fundamentally new perspective is necessary to address these challenges by adopting a holistic, cross-disciplinary approach that bridges the realms of Artificial Intelligence (AI) and Operations Research (OR). This approach will pave the way for the next-generation Decision Intelligence systems, driving sustainable development and economic growth. TwinODIS is a transformative project aimed to revolutionize data-driven large-scale decision optimization through the integration of AI technologies and OR analytical methods. A group of excellence will be established in the emerging field of Large-Scale Decision Intelligence, by twinning the Institute of Computer Science at the Foundation for Research & Technology-Hellas (FORTH, Greece) with two internationally-leading research institutions, namely, the Laboratory of Informatics Paris Descartes, Université Paris Cité (UPC, France) and the Erasmus School of Economics, Erasmus University Rotterdam (EUR, Netherlands). The project leverages comprehensive knowledge transfer, training and networking programmes to elevate FORTH's scientific profile and research capacity, foster an innovation culture to attract industrial investments, engage in competitive EU projects, and enrich the skills of senior scientists in administrative and management aspects, positioning FORTH as a prominent AI-driven Decision Science Hub in Greece and South-Eastern Europe.

Keywords: artificial intelligence,operations research,large scale hybrid optimization,data driven decision support,sustainable development,multi energy systems,smart ports
Engineering QUAntum materials for TErahertz applications (EQUATE)

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: Netherlands
Call: ERC CONSOLIDATOR GRANTS (ERC-2023-COG, 2023)
Start date: 2024-06-01
End date: 2029-05-31
Duration (months): 60.0
Total project cost (EUR): 1999233.0
EU funding (EUR): 1999233.0
Participants: 1
Project status: SIGNED

I propose to demonstrate a novel design paradigm for terahertz (THz) technologies, which places the ultrafast thermodynamic properties of quantum materials at the heart. Approaches with conventional semiconductors and metals, as well as recent attempts with two-dimensional materials, typically aim to exploit the optoelectronic properties of materials towards developing THz components and systems. This has so far not led to the desired breakthrough that the, currently still underdeveloped, terahertz regime so greatly needs. The vision of this project is that the truly game-changing potential of quantum materials for THz applications lies in their exceptional ultrafast thermodynamic properties: Quantum materials, such as graphene and topological insulators, have giant thermodynamic THz nonlinearities, and generate ultrafast and efficient THz-induced thermoelectric photocurrents. They can be combined with photonic structures into quantum metamaterials with strongly enhanced light-matter interaction, and are compatible with established electronic and photonic technologies. Exploiting these and more remarkable properties, we will demonstrate THz emitters, nonlinear THz convertors, and THz detectors - all based primarily on thermodynamic design principles. These will outperform currently available THz components on several fronts, and establish quantum materials as the ideal material platform for integrated THz photonics with novel capabilities. Specifically, we will focus on a combination of quantum materials that are relatively well understood, and novel material systems that are promising yet less well understood. As a result, we expect major impacts both on a fundamental level in terms of understanding ultrafast thermodynamic phenomena, and on an applied level in terms of engineering these thermodynamic properties towards concrete THz technologies. The ultimate goal is to unlock the full potential of the THz range and bring utility and benefit to society.

Keywords: 2d quantum materials,terahertz technologies,ultrafast dynamics
Advancing Green Deal Principles in sMart FacAde TechnoLogies for the construcTion sEctor based on AI, Data & Robotics (AMALTEA)

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: Innovation actions
Partnership: AI-DATA-ROBOTICS (co-programmed)
Countries involved: Spain,Germany,Türkiye,Netherlands,Hungary,Poland,Switzerland,Portugal
Call: Industrial leadership in AI, Data and Robotics boosting competitiveness and the green transition (AI Data and Robotics Partnership) (IA)[[https://www.europarl.europa.eu/RegData/etudes/STUD/2021/662906/IPOL_STU(2021)662906_EN.pdf]] (HORIZON-CL4-2024-DIGITAL-EMERGING-01, 2024)
Start date: 2025-01-01
End date: 2028-12-31
Duration (months): 48.0
Total project cost (EUR): 11204878.75
EU funding (EUR): 9999478.75
Participants: 17
Project status: SIGNED

AMALTEA’s main objective is to streamline the use of robotics, AI and data in the construction sector to reduce the energy use, resources and waste, increase safety, and improve processes’ reliability and product quality to adapt to the new demands from end users and Green Deal objectives. For that, the curtain-wall façade value chain will be reshaped by digitalizing the design, manufacturing, installation and disassembly/recycling phases. This approach is supported by the development and combination of several digital tools that will be integrated, together with technical solutions provided by SMEs and start-ups through FSTP, in Digital Solutions that will be validated in four pilots to improve the sustainability, efficiency, safety and automation of the façade value chain, and thus, building construction. The global solution will consider human in the loop for collaborative robotics and will use existing technologies to guarantee the safety use of the data across the entire process. Piloting will include: 1) AI parametric design and extended digital twin with all the data workflows along the façade value chain, 2) Digitalized façade manufacturing line in FDB facilities in Hungary, 3) and 4) Implementation of the solutions in operational construction environments (Residential/Hospital typology in the South of Europe and Offices/Tertiary typology in the Center of Europe). The project involves companies from all necessary disciplines, including: design, AI and data, robotics, manufacturing, construction, SSH (to have special care related to the communication between humans and the technologies developed). Additionally, the project will establish a collaboration with external SMEs (up to 25 projects) across Europe through a FSTP scheme to improve all the challenges defined.

Keywords: robotics,ai,data,construction,facades,mobile platform,end effector,green deal