Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Biomedical MXenes as cell labeling and tracking platforms (BIO-MX)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Materials Engineering
Panel description: Advanced materials development: performance enhancement, modelling, large -scale preparation, modification, tailoring, optimisation, novel  and combined use of materials, etc.
Countries involved: Italy
Call: ERC ADVANCED GRANTS (ERC-2024-ADG, 2024)
Start date: 2026-01-01
End date: 2030-12-31
Duration (months): 60.0
Total project cost (EUR): 2499752.0
EU funding (EUR): 2499752.0
Participants: 1
Project status: SIGNED

WHY Visualizing, labeling, and tracking cells is critical for advancements in biomedical research, especially in areas like cancer treatment and cell therapy. Over the past two decades, practices for cell labeling strategies have remained unchanged, facing the same critical technological limitation: a lack of versatility. By using a multidisciplinary approach to create MX-tags, a new class of nanomaterials, BIO-MX aims to overcome this limitation to accelerate biomedical discoveries and protect human health. WHAT MX-tags will create an exciting chemical scenario coupled with single-cell mass cytometry (CyTOF), a revolutionary technology in cell biology. Although CyTOF has shaped the current understanding of cellular matter, fundamental needs remain to be addressed to enable new discoveries: 1. To develop universal cell tags that preserve cell functionality, are compatible with all cell types, and integrate seamlessly with advanced single-cell approaches. 2. To produce highly abundant chemically versatile tags able to multiplex the information on cells going beyond current limited available chemistries. 3. To create sensitive and multi-function imaging tags to label, track, and provide spatial resolution of cells for cancer applications. HOW My ground-breaking idea is based on transforming the chemistry of 2D materials called MXenes to create MX-tags, a new class of multi-modular materials involving the peculiar properties of cell-penetrating peptides and nanobodies that will open new horizons for mass cytometry-based discoveries. My unique expertise in CyTOF, immune cells, and 2D material development will drive the key steps of the project. My hypotheses rely on the future use of MX-tags as labeling, tracking, and multi-imaging tools. BIO-MX will provide transformative approaches and methods poised to contribute to human health.

Keywords: mxenes,single cell mass cytometry,cytof
Net Zero Persikan: emission-free ground works and construction (Net Zero Persikan)

Framework: Horizon Europe — Innovative Europe
Programme: The European Innovation Council (EIC)
Theme: EU prize
Destination: European Innovation Procurement Awards
Action type: Recognition Prize
Countries involved: Sweden
Call: Facing societal challenges category: - “Net Zero Industry Procurement” (HORIZON-EIC-2024-PRIZE-EIPA, 2024)
Total project cost (EUR): 0.0
EU funding (EUR): 0.0
Participants: 1
Project status: CLOSED

Net Zero Persikan used innovation procurement to enable emission-free ground works and construction. The project is demonstrating a fully-electric machine pool in ground works at a construction site in Stockholm, Sweden. The project addresses a phase (so-called “fine planning” of the streets and public spaces in an area of around 67,000m2) in the development of Kv. Persikan (the "Peach") on east Södermalm in Stockholm. During spring 2023, a competitive dialogue took place and resulted in procurement of a contractor with works starting in October 2023. The project is demonstrating working practices, methods and techniques which can accelerate the transition to emission-free ground works and reduce noise and environmental pollution will be reduced. The project is a forerunner and provides inspiration to the construction sector and municipalities, as stakeholders gain knowledge and experiences on emission-free ground works and fully-electric operations. The project has already received significant interest from national ministries, municipalities in Scandinavia and Asia, major vehicle and machinery providers, along with other stakeholders. By demonstrating how to execute an innovation procurement and implement its outcome in a complex urban environment, Net Zero Persikan shows how public sector procurers can stimulate emission-free construction and works, thereby accelerating the transition to a Net Zero Europe.

Keywords: innovation procurement competitive dialogue construction machinery transport emissions noise digitalisation digital twinning battery storage charging infrastructure energy
Ferroelectric Leaky Integration for Computational Efficiency (FELICE)

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: Netherlands
Call: ERC PROOF OF CONCEPT GRANTS (ERC-2025-POC, 2025)
Start date: 2026-01-01
End date: 2027-06-30
Duration (months): 18.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: SIGNED

The rapid growth of AI demands efficient hardware solutions that enable real-time learning and decision-making in power-constrained edge applications, such as autonomous systems, medical diagnostics, and industrial monitoring. However, current AI hardware faces fundamental trade-offs between power consumption, processing efficiency, and accuracy, limiting its deployment at the edge. Traditional computing architectures separate memory and processing, leading to high energy costs associated with data movement, while existing neuromorphic and analog AI solutions often lack efficient on-chip learning capabilities. FELICE introduces a breakthrough approach to edge AI hardware by co-designing a novel computing architecture that integrates processing and memory within a single compact system. By leveraging ferroelectric leaky integrate-and-fire (FeLIF) neurons and FeFET-based non-volatile memory (NVM), FELICE enables efficient temporal information processing and real-time learning at significantly lower power and area requirements compared to conventional designs. This eliminates the need for external training, allowing AI models to adapt dynamically to changing conditions, a key advantage for edge applications operating in unpredictable environments. A core innovation of FELICE is its training methodology, which optimizes learning at different time scales, overcoming key limitations in temporal AI processing. Furthermore, the 28 nm technological node enhances energy efficiency, making FELICE a scalable and commercially viable solution. By integrating hardware and software co-optimization, FELICE extends the capabilities of current CMOS technology to enable energy-efficient AI deployment at the edge, offering a transformative step towards compact, adaptive, and real-time AI solutions in high-impact domains.
Tracking the North Atlantic water circulation during the Last Glacial period using multi-proxy approaches (NAcMupro)

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: Denmark
Call: MSCA Postdoctoral Fellowships 2022 (HORIZON-MSCA-2022-PF-01, 2022)
Start date: 2024-09-01
End date: 2026-08-31
Duration (months): 24.0
Total project cost (EUR): 214934.4
EU funding (EUR): 214934.4
Participants: 1
Project status: SIGNED

Ocean circulation is a key regulator of climate that driven by both atmospheric and thermohaline processes. The Labrador Sea holds a complex mixture of water masses and plays a crucial role in North Atlantic Deep Water formation and the Atlantic Meridional Overturning Circulation, and thus for global ocean circulation. However, the mechanisms of the Labrador Sea Water formation and its interactions with upper North Atlantic Deep Water remain largely unknown. Understanding the water formation of the Labrador Sea Water and ocean circulation in the North Atlantic region in historical time by using proxy reconstruction data is crucial for benchmarking and understanding the present and future variations using model simulations. The purpose of this proposal is to provide information on the evolution of the North Atlantic intermediate water formation and the impact of changes on ocean circulation in the Labrador Sea during several cold periods including the Last Glacial Maximum, understanding of North Atlantic water inflow, the Labrador Sea deep water ventilation conditions and formation respond to the fundamentally different glacial cold events. By combining benthic foraminiferal morphology and geochemistry data, and other existing proxies, this project will discover the link between shell morphology and multiple stresses, and quantitatively evaluate the impact of changes in the ocean circulation on marine ecosystems. The project aims to apply a multiproxy approach including the application of foraminiferal morphology and geochemistry to reconstruct the seawater conditions. I will generate new high-resolution records of changes in subsurface to deep-water formation in the Labrador Sea region to evaluate the impact of changes in ocean circulation and to significantly improve the application of this multiproxy approach. Results from this project will also increase the data window of oceanic circulation for climate model validation.

Keywords: paleoceanography,north atlantic ocean,micropaleontology,last glacial maximum,foraminifera
Ekkono Synthesis: Federated Learning for the Industrial IoT (Ekkono FLIIoT)

Framework: Horizon Europe — Innovative Europe
Programme: The European Innovation Council (EIC)
Theme: EIC Accelerator
Destination: Challenges
Action type: EIC actions
Countries involved: Sweden
Call: EIC Accelerator Challenge: Technologies for Open Strategic Autonomy (HORIZON-EIC-2022-ACCELERATOR-01, 2022)
Start date: 2023-05-01
End date: 2025-12-31
Duration (months): 32.0
Total project cost (EUR): 3571250.0
EU funding (EUR): 2499875.0
Participants: 1
Project status: SIGNED

Ekkono is developing a unique product: Edge Intelligence, an Edge Machine Learning framework that conducts learning on super-small, sensor-sized devices, enabling a transition from ‘connected’ to ‘smart’. It is based on over 7 years of university research, and is materially different from other AI solutions that either run in the cloud or can only perform inference onboard of connected things (IoT). Our product trains an individual model on each unit it runs on, learning how, where and for what the unit is used—enabling per-device maintenance, performance & optimization. We are pioneering Edge AI in the emerging Industrial IoT 2.0, specializing in the European industrial manufacturing & automotive sectors. Our ambition is to disrupt the market with our Federated Learning solution, called Synthesis, where individual device models are collected, compared, correlated & combined into supermodels, enabling next-gen sustainability leap & new service-centric business models for product OEMs.

Keywords: predictive maintenance,edge computing,federated learning,machine learning,industrial iot,energy efficient algorithms
Semantics of Software Systems (S3)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Computer Science and Informatics
Panel description: Informatics and information systems, computer science, scientific computing, intelligent systems
Countries involved: Germany
Call: ERC ADVANCED GRANTS (ERC-2022-ADG, 2022)
Start date: 2023-09-01
End date: 2028-08-31
Duration (months): 60.0
Total project cost (EUR): 2500000.0
EU funding (EUR): 2500000.0
Participants: 1
Project status: SIGNED

*What if we had software bots that tirelessly test, debug, and monitor our software systems?* IT workers are expensive and scarce. So why can't we further automate boring, repetitive activities such as testing and debugging? The problem is that we lack computer-readable _specifications_ (so-called _oracles_) for what the system should do or not do. For decades, this _oracle problem_ has been a roadblock to automated test generation, trusted software repairs, and accurate monitoring of software. Building on groundbreaking research to infer input languages of systems, S3 introduces a unified approach to _learning oracles automatically_. It takes a given software system; _infers_ and _decodes_ its inputs and outputs; and runs _experiments_ to extract _models_ of how the system behaves, capturing its semantics by predicting output features for given input features. These models, named _system invariants_, allow to _fully automate_ critical software development activities: TESTING. System invariants encode _languages_ for automatically generating test inputs and provide _oracles_ for checking test results: "In the TLS server, the <payload> in the <heartbeat-response> must be the same as in the <heartbeat-request>." DEBUGGING. System invariants allow narrowing down causes of software behavior ("The X.509 public key certificate is not recognized if <subject-name> contains a zero byte"). Generated tests and oracles ensure reliable automated repair. MONITORING. System invariants enable detecting abnormal behavior at runtime ("In 'log4j', logging a <user-agent> containing '"${jndi:<url>}"' opens <url>"). Problematic queries can be isolated and investigated until the problem is fixed. In the future, testing, debugging, and monitoring would thus be taken over by _software bots_ who would autonomously explore software behavior, report issues, and suggest actions to their human co-workers, boosting developer productivity and software reliability.

Keywords: software test generation,fuzzing,software debugging,software repair,monitoring,data formats
GLYCANS AS MASTER TRIGGERS OF HEALTH TO INTESTINAL INFLAMMATION TRANSITION (GlycanTrigger)

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: Portugal,Netherlands,France,Germany,United States,Belgium,United Kingdom
Call: Personalised blueprint of chronic inflammation in health-to-disease transition (HORIZON-HLTH-2022-STAYHLTH-02, 2022)
Start date: 2023-01-01
End date: 2028-12-31
Duration (months): 72.0
Total project cost (EUR): 6771571.0
EU funding (EUR): 6771571.0
Participants: 10
Project status: SIGNED

Chronic inflammation underlies several diseases. In Crohn´s disease (CD), there is mounting evidence of a preclinical phase characterized by immunological changes that precede symptoms. Here, we propose a thorough and innovative approach to better understand the health-to-chronic inflammation transition occurring in patients with CD that will be translated in improved disease prediction and prevention. We will address how changes in glycosylation of the gut mucosa act as a primary event that dysregulates not only local mechanisms (imposing maladapted host-microbiome interactions) but also systemic mechanisms (promoting anti-microbial antibodies production), involving the novel concept of glycan mimicry as an early trigger of the health to inflammation transition – GlycanTrigger. Capitalizing on our recent findings showing that changes in the expression of complex branched N-glycans at gut mucosa regulate Inflammatory Bowel Disease immunopathogenesis, we here hypothesize that changes in gut glycocalyx impose early perturbations in host-microbiome interactions, leading to the exposure of altered (glyco)neoantigens, both by pathogens and host epithelial cells which trigger the activation of innate immune response. Moreover, following our recent discovery that changes in glycosylation can be detected in serum years before CD diagnosis and are correlated with increased levels of pathogenic antimicrobial antibodies (ASCA), we will here pursue the hypothesis that changes in mucosa glycans constitute a major source for the development of anti-glycan antibodies (ASCA), associated with health-to-inflammation transition. The long-term GOAL of this proposal is to unlock, through an integrative approach, a new checkpoint that regulates the transition to chronic inflammation. Our proposal will lead to breakthrough concepts in the health to chronic inflammation transition and to novel predictive tools translated in novel glyco-intervention strategies for disease prevention.

Keywords: glycosylation microbiome innate immune response anti glycan antibodies asca biomarkers personalized prevention social innovation
Characterizing dynamics and drivers of resilience across scales in Patagonian rangelands (REPAIR)

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,Argentina
Call: MSCA Postdoctoral Fellowships 2023 (HORIZON-MSCA-2023-PF-01, 2023)
Start date: 2026-01-01
End date: 2027-12-31
Duration (months): 24.0
Total project cost (EUR): 181152.96
EU funding (EUR): 181152.96
Participants: 2
Project status: SIGNED

Increasing aridity due to climate change is a major factor that has dramatically altered the structure of Patagonian drylands, which has resulted in changes in ecosystem structure and functioning, e.g., a loss of vegetation, biodiversity, and soil erosion. Such changes ultimately lead to a loss of resilience, the system's ability to resist and recover from external disturbances while retaining the same function and structure. Although our ability to monitor resilience at large scales has been accelerated by remote sensing data and techniques, greater insights into the role of certain biotic and abiotic variables (e.g., species richness or soil carbon) on resilience at the local scale, would create new opportunities to support sustainable management to increase resilience across Patagonia. In this project, I will bridge these scales, by linking remote sensing time series data and a unique database (MARAS) of continuous field observations across Patagonian rangelands (managed by Argentinian partners). Specifically, I will 1) characterize changes in resilience in response to increasing aridity based on remote sensing between 2000 - 2025, 2) collect and analyze ground observations, and 3) investigate drivers of resilience dynamics across Patagonian rangelands integrating the remote sensing-based resilience and field data. To achieve this, I will use state-of-the-art methods in ecology and data science (e.g., time series analysis and machine learning) to deliver a better understanding of which environmental variables impact resilience in what way. I will carry out the research at the Complutense University of Madrid (Spain) at the Department of Biodiversity, Ecology and Evolution, under the guidance of Dr. Miguel Berdugo; and I will undertake a secondment with the Soil Conservation research group at the National University of Luján (Argentina) under the supervision of Prof. Dr. Juan José Gaitán.

Keywords: resilience,vegetation dynamics,drylands,field observations,climate change,aridity
MultimoDal tEnsor fuSion and Completion foR patient-taIlored catheter aBlation of persistEnt atrial fibrillation (DESCRIBE)

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: France
Call: MSCA Postdoctoral Fellowships 2023 (HORIZON-MSCA-2023-PF-01, 2023)
Start date: 2025-01-01
End date: 2025-12-31
Duration (months): 12.0
Total project cost (EUR): 105877.44
EU funding (EUR): 105877.44
Participants: 1
Project status: SIGNED

Tensor analysis plays a central role in signal processing and machine learning for the representation, analysis, fusion, and classification of data. Responsible for up to 25% of brain strokes, atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia and remains the last great frontier of cardiac electrophysiology. Catheter ablation is the most attractive therapeutic option for persistent AF, although the identification of suitable target areas is strongly dependent on practitioner’s subjectivity. Multi-electrode catheters are increasingly used in ablation as they facilitate the electroanatomical mapping of the atria, but often deliver incomplete data due to lack of contact with the atrial wall. This project aims to improve the personalized characterization and management of AF by proposing novel tensor-based methods for multimodal data fusion in a possibly missing information scenario. New coupled tensor models will be introduced for effectively coupling multimodal information and robust optimization algorithms will be developed for retrieving unknown/unavailable information. It is expected that the optimal exploitation of invasive (intracardiac EGM) and noninvasive (surface ECG) records will allow the automatic identification of the best targets for successful ablation. Encouraging preliminary results have been obtained with the block-term decomposition (BTD) to handle multiple time segments of the ECG for the blind separation of the atrial activity signal. The contribution of EGM into the ECG will be identified by analyzing the common factors obtained by the proposed coupled tensor decompositions. Extensions of coupled BTD to multimodal, possibly missing data will also be proposed. Expected impacts lie in original tensor models and algorithms for data fusion and tensor completion, leading to novel descriptors of AF that can significantly advance the understanding of this prevalent cardiac condition and derive patient-tailored ablation protocols.

Keywords: tensor decompositions,data fusion,tensor completion,atrial fibrillation,catheter ablation,electrocardiogram,electrogram