Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Shaping cortical computations via higher-order feedback (FeedbackCircuits)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Neuroscience and Disorders of the Nervous System
Panel description: Nervous system development, homeostasis and ageing , nervous system  function and dysfunction , systems neuroscience  and modelling , biological basis of cognitive processes and of behaviour, neurological and mental disorders – In humans and all other organisms
Countries involved: Germany
Call: ERC CONSOLIDATOR GRANTS (ERC-2024-COG, 2024)
Start date: 2025-09-01
End date: 2030-08-31
Duration (months): 60.0
Total project cost (EUR): 1818781.0
EU funding (EUR): 1818781.0
Participants: 1
Project status: SIGNED

Our ability to flexibly adapt to changing environments depends on how we perceive, prioritize and act on stimuli. This involves actively integrating our current sensory experiences with our prior knowledge of the world and the surrounding context. Stimulus perception is influenced by contextual top-down signals from areas higher up in the processing hierarchy that carry information about internal state, attention and future actions to early processing stages where they are combined with bottom-up inputs. Commonly referred to as “feedback”, these top-down signals are multi-faceted; they come from diverse brain areas and are integrated at different loci in neural circuits. What type of information they carry and where is still unclear despite their fundamental role in shaping even the most mundane tasks. FeedbackCircuits will investigate the mechanistic circuit basis of feedback-driven cortical computations, including contextual modulation and the amplification of unexpected stimuli, and will pinpoint the synaptic plasticity mechanisms governing the wiring logic of feedback projections. Constrained by experimental data from the mouse visual cortex, I will build a multi-scale theoretical framework that unifies diverse experimental findings and links cellular to circuit-level processing. Our strategy leverages new datasets that encompass multiple modalities, from neural responses in various brain regions to detailed synaptic-level wiring diagrams. The proposed mechanistic models will enable the exploration of distinct feedback sources and sites of plasticity, and together with the data, define plausible parameter spaces underlying feedback-driven computations. In contrast to other efforts training hard-to-interpret artificial neural networks, our models promise to elucidate the mechanistic underpinnings of circuit structure-function dynamics involving feedback, to distinguish between competing mechanistic hypotheses and make numerous experimental predictions.
Metal Additive Manufacturing with sinter-based Cold Metal Fusion Technology for mass production of metal parts (CMF)

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: Germany
Call: EIC Accelerator Open 2021 (HORIZON-EIC-2021-ACCELERATOROPEN-01, 2021)
Start date: 2022-04-01
End date: 2024-03-31
Duration (months): 24.0
Total project cost (EUR): 2999375.0
EU funding (EUR): 2099562.0
Participants: 1
Project status: CLOSED

Commercial AM technologies for metal parts have restricted material selection and high production costs that limit their application to prototypes and small batches. CMF is a patented process to produce metallic parts, in which we successfully merge the sinter-based approach used in Powder Metallurgy with the Selective Laser Sintering, the AM equipment widely used to form plastics parts. Our innovation is based on our unique metal feedstock that can be processed on existing systems for plastics in the market. With this new approach, we offer metal manufacturers: i) An innovative technology that provides the freedom and adaptability to produce more complex parts, with a lower CAPEX (ca. 95%) as we have adapted the process for the cost-efficient plastic AM machines in the market. ii) The series production of metal parts via 3D printing with cost-saving potential of up to 90% when scaling production to larger series (>100,000 parts) and available with a great variety of materials.

Keywords: metal additive manufacturing,metal feedstock,sinter based,metal injection moulding,selective laser sintering,powder metallurgy,alloys,metal,large series
Transceivers for Analog Photonic Computation (TRANSBIT)

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: Spain
Call: ERC PROOF OF CONCEPT GRANTS (ERC-2025-POC, 2025)
Start date: 2025-10-01
End date: 2027-03-31
Duration (months): 18.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: SIGNED

Analog Programmable-Photonic Computation (APC) is a novel paradigm that exploits programmable integrated photonics (PIP) for high-performance optical computing and signal processing. The ERC-funded ANBIT project established APC’s foundation by defining the analog bit (anbit), introducing computational operations, and designing circuit implementations. However, a major challenge remains: the efficient generation and detection of anbits. Existing solutions rely on fixed optical components, making them susceptible to fabrication errors and operational drifts, limiting scalability and precision. TRANSBIT addresses this challenge by introducing reconfigurable photonic transceivers for anbit encoding and detection. By replacing static beam combiners with tuneable basic units of PIP, TRANSBIT enables real-time adaptive error correction, ensuring stable and high-fidelity operation. This approach not only enhances APC’s computational capabilities, but also facilitates its scalability and practical deployment. By overcoming a fundamental bottleneck, TRANSBIT paves the way for the transfer of APC technology to real-world applications. Its impact extends beyond computing, offering new opportunities in optical communications and neuromorphic photonic networks. This project marks a key step toward the integration of APC into next-generation photonic systems, unlocking its full potential for future technological advancements.
Harnessing advanced computational and synthetic biology to understand phage lysin complexity (COMPLENDO)

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: Spain,Belgium
Call: MSCA Postdoctoral Fellowships 2024 (HORIZON-MSCA-2024-PF-01, 2024)
Start date: 2025-11-01
End date: 2027-10-31
Duration (months): 24.0
Total project cost (EUR): 194074.56
EU funding (EUR): 194074.56
Participants: 2
Project status: SIGNED

(Endo)lysins are phage-encoded enzymes that degrade the peptidoglycan layer of bacterial cell walls, facilitating the release of viral progeny. Both wild-type and engineered lysins have shown promise as alternatives or complements to antibiotics, of importance in light of rising antimicrobial resistance. However, current lysin research and development (R&D) efforts remain largely empirical, needing resource-intensive screening processes that often yield limited generalizable insights. This challenge is further compounded by the recent failure of the first clinical trial intended as proof-of-concept for lysins as antimicrobials, alongside new findings that demonstrate an unexplored complexity of lysins. This makes the case for an investment in fundamental research to address unresolved questions about lysin structure and function. Specifically, this project will explore two major issues related to lysins from the two major bacterial macrostructural paradigms, namely: (i) the specificity determinants of enzymatically active domains (EADs) in Gram-positive lysins and (ii) the role of cell wall binding domains (CBDs) and antimicrobial peptide-like elements (AMLEs) in Gram-negative lysins. To achieve this, I will deploy, for the first time in the field, advanced computational methods to generate fully synthetic lysins that will serve as scaffolds for testing specificity determinants in EADs. In addition, I will develop an "in virio" testing platform (IVTP) in collaboration with a research network that this project will help establish. The IVTP will be central to the project, allowing for the evaluation of functional diversity within the context of the phage-host interaction, thus overcoming the applied bias in current lysin research. Ultimately, this project will deepen our understanding of the newfound complexity of lysins and contribute to the rationalization and de-risking of R&D pipelines addressing one of the 21st century's major healthcare challenges.

Keywords: bacteriophages,endolysins,protein engineering,computational protein design,genome editing,antimicrobials
Next Generation Internet Discovery and Search (NGI Search)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Digital, Industry and Space
Theme: Digital
Destination: A human-centred and ethical development of digital and industrial technologies
Action type: Research and Innovation actions
Countries involved: Denmark,Poland,Spain,France
Call: Trustworthy open search and discovery (RIA) (HORIZON-CL4-2021-HUMAN-01, 2021)
Start date: 2022-09-01
End date: 2025-08-31
Duration (months): 36.0
Total project cost (EUR): 8499531.25
EU funding (EUR): 8499531.25
Participants: 6
Project status: SIGNED

Since the inception of the internet, applications and services using the internet have evolved significantly. In addition the number of devices connected to the internet, the creation of vast data lakes as well as distributed data cooperatives has made searching and discovering data and generally resources a difficult, yet very important field of research, development and innovation. Today it is even more important to be able to find, represent and ultimately make sense of internet based data sources and resources without the need of relying on non-transparent organisations offering such services, potentially violating privacy and trust. The vision of the NGI Search project is to change the way we use and experience search & discover data and resources in general on the internet and web. This includes new user interfaces for searching and representing data (e.g. voice and image based), heterogeneous data sources (e.g. IoT, semantic data, multimedia, social media as well as traditional websites), new methodologies (e.g. machine learning and natural language processing) in generic context and specific use cases (e.g. industry 4.0, health, social media).

Keywords: machine based search internet of things
Low Resource Artificial Intelligence (lorAI)

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: Slovakia,Ireland,Germany,Greece
Call: Teaming for Excellence (HORIZON-WIDERA-2023-ACCESS-01, 2023)
Start date: 2025-03-01
End date: 2031-02-28
Duration (months): 72.0
Total project cost (EUR): 13999950.0
EU funding (EUR): 13999950.0
Participants: 4
Project status: SIGNED

The main objective of the lorAI project is to upgrade the Kempelen Institute of Intelligent Technologies (KInIT) to a leading R&I institution in low resource artificial intelligence (LRAI) in Slovakia and Europe (with help of advanced partners – top European AI research institutes ADAPT, DFKI and CERTH). The core of lorAI’s impact stands on three pillars: 1) nurturing talent and personal capacities, 2) pursuing research excellence in low resource AI (LRAI), and 3) innovation and technology transfer with elaborated outreach programmes. LRAI represents a global research challenge yet is also relevant to the EU region by developing efficient AI operating with limited resources to increase AI availability (for society and industry) and environmental sustainability. Despite tackling the low complexity and computational costs of AI, Centre of Excellence will explore AI applications for the NLP and Green environment (i.e., domains of energy usage optimization, anomaly detection). The activities of the CoE are grouped into ten focused Excellence programs, which, in synergy, contribute to expected impacts overseen by the call and destination by increasing research capacities and competence, funding acquisition capacities, excellent research with societal and economic impact, engagement of industry and wider R&I ecosystem, and internationalization. Aligned to Slovak and EU strategies (e.g., Slovak Smart Specialization, Coordinated Plan on AI, Green Deal), the lorAI project builds on a unique opportunity of ongoing programs (e.g., EUs Recovery and Resilience), importance of AI and environmental challenges, to multiply effects of the measures resulting in improving Slovak R&I culture and stimulate reforms. Thanks to the CoE governance model, autonomy is guaranteed. The diversified budget reduces unexpected disruptions and with cross-cutting themes (international, interdisciplinary, cross-sectoral, transfer oriented) ensures long-term sustainability.

Keywords: excellent research innovations artificial intelligence low resource ai environmental sustainability natural language processing international interdisciplinary cross sectoral trustworthy ai
AI-CODE - AI services for COntinuous trust in emerging Digital Environments (AI-CODE)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Digital, Industry and Space
Theme: Digital
Destination: A human-centred and ethical development of digital and industrial technologies
Action type: Innovation actions
Countries involved: Italy,Greece,Spain,Germany,Slovakia,Lithuania,Netherlands,Belgium
Call: Through AI from Disinformation to Trust (IA) (HORIZON-CL4-2023-HUMAN-01-CNECT, 2023)
Start date: 2023-12-01
End date: 2026-11-30
Duration (months): 36.0
Total project cost (EUR): 5616839.47
EU funding (EUR): 4969471.25
Participants: 17
Project status: SIGNED

The media sector is exposed to and undergoing continuous innovations that occur at a pace never seen before and have a non-negligible impact on citizens, democracy and a society as whole. A significant booster becomes a generative Artificial Intelligence, which already plays and will continue to play a critical role (in a positive as well as negative meaning) in creating and spreading information. Especially in next-generation social media, which refer to the anticipated evolution towards more AI-based decentralised and immersive virtual environments (like fediverses and metaverses), generative AI can become the most prominent enabler of disinformation growth accompanied by a lack of trusted information. Media professionals are not, however, currently well-equipped with supporting tools nor knowledge to operate in such already emerging environments. As a result, there is a tremendous need for innovative (AI-based) solutions ensuring media freedom and pluralism, delivering credible and truthful information as well as combating highly disinformative content. The main goal of the AI-CODE project is to evolve state-of-the-art research results (tools, technologies, and know-how) from the past and ongoing EU-funded research projects focused on disinformation to a novel ecosystem of services that will proactively support media professionals in trusted information production through AI. First, the project aims to identify, analyse, and understand future developments of next-generation social media in the context of rapid development of generative Artificial Intelligence and how such a combination can impact the (dis)information space. Second, the project aims to provide media professionals with novel AI-based services to coach them how to work in emerging digital environments and how to utilise generative AI effectively and credibly, to detect new forms of content manipulation, as well as to assess the reputation and credibility of sources and their content.

Keywords: disinformation,media professionals,generative ai,next generation of social media,coaching media professionals,detect new forms of content forging and tampering,trustwothiness of ai models
Advanced learning for generalizable and reliable robotic skills (ALGR2S)

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: United Kingdom
Call: MSCA Postdoctoral Fellowships 2024 (HORIZON-MSCA-2024-PF-01, 2024)
Start date: 2026-04-01
End date: 2028-03-31
Duration (months): 24.0
Total project cost (EUR): 260347.92
EU funding (EUR): 260347.92
Participants: 3
Project status: SIGNED

Robotics is crucial in Europe for driving innovation and maintaining global competitiveness in advanced manufacturing, service and automation, etc. At present, one of the primary limitations hindering the further development of robotics today is the lack of sufficient intelligence, which prevents robots from being deployed in complex tasks such as those in home-services, and healthcare. Robotic skill learning aims to provide robots with the necessary intelligence to acquire the skills needed for performing these complex tasks. The explosion of AI technologies in recent years has provided a large platform and space for robot skill learning. The primary objective of this proposal is to develop a generalizable robotic skills learning system leveraging advanced AI techniques. This system will be designed to emulate the human skill-learning process, thereby enhancing both the reliability and explainability of the learning behaviors. The researcher will investigate foundation model techniques to provide human-level generalization ability for robots. Moreover, The researcher will present novel biologically inspired sensorimotor policy learning algorithms to significantly enhance the reliability and explainability of the system. The system will be validated via various applications such as assembly across different objectives. During the project, the researcher will acquire a solid multidisciplinary scientific and transferable skills training, covering from AI theorems, robotic applications, project management skills and career planning, etc. The final aim of the project is to push the Europe in a good position to ride the coming wave of "AI + Robots".

Keywords: robotics,learning from demonstration,imitation learning,ai,sensorimotor policy,dynamic system learning
Quantifying polygenic selection in real-time adaptation to climate change (Poly2Adapt)

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: Italy
Call: MSCA Postdoctoral Fellowships 2021 (HORIZON-MSCA-2021-PF-01, 2021)
Start date: 2023-05-01
End date: 2025-04-30
Duration (months): 24.0
Total project cost (EUR): 188590.08
EU funding (EUR): 188590.08
Participants: 2
Project status: CLOSED

Understanding if and how species are able to rapidly adapt to climate change is a major and challenging problem of modern biology. Can evolution operate quickly enough? Theory suggests that the genetic basis of most phenotypic traits is polygenic, and evolution can be rapid if it harnesses this reservoir of standing variation spread across the genome - ‘Poly2Adapt’. Recent theoretical advances suggest that the signature of real-time polygenic selection can be identified using time-series genomic data. Moreover, a recent paradigm-shift, framed in the ‘omnigenic model’ suggests that all genes which are broadly expressed, or in general all genomic regions broadly available in euchromatin, contribute to the heritability of complex traits. How can we use these theoretical advances in concert? Benefiting from a unique genomic time-series dataset comprising 1,000 samples from 2000 to 2021, the aim of Poly2Adapt is to quantify the polygenic variation relevant to real-time adaptation to climate change in the King penguin, a keystone species occupying one of the most climate change-impacted ecosystems. Coupling genomic time-series analysis with gene expression and chromatin accessibility data, I will rigorously test the theories of the polygenic (O1) and omnigenic (O2) models, using this information to develop metrics of adaptation potential (O3) for the conservation of other at-risk species. This will be the first study to detect the footprints of real-time adaptation proposed by the polygenic and omnigenic models in a wild species. The objectives are directly relevant to the priorities of Horizon Europe, ‘to tackle climate change’, in particular Mission 1: Adaptation to Climate Change. I will harness interdisciplinary expertise from the host, secondment and collaborators to deepen my skills in evolutionary theory, bioinformatics, and population and conservation genetics. Together, we are well-positioned to employ powerful new approaches to pursue this timely research.

Keywords: climate change,evolutionary genomics,polygenic selection,population genomics