Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : 3DBAG (Horizon Europe, appel 2022, Proof of concept grants)
Entité participante : Université de technologie de Delft — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : TU Delft
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 90000.0 €

In the ERC project Urban Modelling in higher dimensions (UMnD) we developed a solution for detailed 3D Building models Automatically Generated for very large areas (3DBAG). This prototype was applied to generate detailed 3D models for all 10 million buildings in The Netherlands. Such a digital twin of built objects provides a valuable data source and simulation basis for professionals working in planning, design, construction, energy, real estate, as well as for environmental impact studies on noise, floodings, wind flows, air quality and urban heat islands. 3D models are already used in specific domains, but it takes a lot of effort to generate suitable 3D building models for a given application due to the required manual editing. Therefore, detailed 3D models are rarely available for large areas (regions or nations). The solution lies in the unique method we developed, which enables to generate high-quality, easy-to-use 3D building models fully automatically at national scale to be used without any technical or legal restriction. This will be a breakthrough for the use of 3D data in current and new applications. With the beta-release of the Dutch 3DBAG and the subsequent wide adoption and application of it by industry and government, the effectiveness of 3DBAG has been demonstrated. This PoC aims to further advance 3DBAG from the current TRL6 to TRL9. Firstly, we will make 3DBAG technically sustainable by addressing issues to achieve even higher adoption, supporting more height data sources as available in other countries and to prepare for the maintainability and performance of our method in an operational setting. Secondly, we will explore how to best ensure the economical sustainability of 3DBAG by clarifying the IPR and licencing strategy and assessing the market potentials (extending our current relations), the users’ interest to pay for value added services and possible international expansions.
Projet ERC : MAGMATED (Horizon Europe, appel 2025, Proof of concept grants)
Entité participante : Université Charles-III de Madrid — Pays : Espagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Natural hydrogels and bioinspired soft materials are changing paradigms in regenerative medicine, drug delivery, microfluidics and soft robotics applications. Their use requires a comprehensive mechanical characterisation over time that is currently limited by the need of several and costly experimental setups. These equipment faces difficulties for imposing hydrating and thermal conditions, and for gripping samples during testing without damaging them or introducing undesirable stress concentrations that compromise the validity of the results. Another research area that faces the same issues and limitations is the mechanical testing of ex vivo tissue for diagnosis. MAGMATED platform will comprise an intelligent system and integrated data-driven method to mechanically characterise soft materials and biological tissues, providing a fast spatial distribution of apparent stiffness and viscoelastic properties. It builds on our results on magneto-active polymers (MAP) and magnetic actuation control to induce deformation under realistic in vitro conditions. The platform will integrate MAPs as sample holders with a computational framework combining data-driven and physical models to determine the spatial distribution of mechanical properties, from recorded deformation images. Multiple force combinations on the sample, along with inverse engineering approaches, will enable a comprehensive and accurate spatial characterization surpassing existing methods. MAGMATED has the potential to significantly reduce testing times and costs for mechanical characterisation of soft biological materials and tissues, providing a portable platform that does not require extensive user’s expertise. We expect a profound impact on testing and design protocols in bioinspired scaffolds, regenerative medicine or diagnosis. The simplified and cost-effective system will also broaden access to mechanical testing across diverse facilities, eliminating the need for dedicated laboratory space.

Mots-clés : magneto mechanics,mechanobiology,machine learning,diagnostics,soft materials,experimental characterisation
Projet ERC : SALSA (Horizon Europe, appel 2022, Proof of concept grants)
Entité participante : Université de La Corogne — Pays : Espagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

One of the key aspects of any successful business is knowing how customers feel about its brand and products. For this purpose, sentiment analysis or opinion mining tools could be paramount in helping companies to succeed. However, the current state of the art sentiment analysis solutions (both commercial or academic) present important drawbacks including low accuracy, low performance (response time around 100-1000 ms), high computational cost and/or high price (around €500/month on average) relegating these solutions to consolidated big brands, social listening agencies or consulting firms offering social listening services.  SALSA aims to democratize the analysis and transformation of internet/social data into knowledge creation for decision-makers, making large-scale sentiment analysis technology viable for small entities without massive computational power. SALSA will explore the potential of the powerful models and algorithms developed within ERC Starting Grant FASTPARSE to create the first AI-based syntax-guided sentiment analysis engine which is: a) accurate, due to using syntactic information to infer the opinions contained in each sentence from its structure and the relationship between its words, rather than shallow methods that consider words in isolation and b) cost-effective, due to employing fast parsers that have a throughput in the order of 1000 sentences per second on consumer-grade hardware, and that can work without time- and memory-hungry large language models.  SALSA will follow an open-source software business model in which we will explore several sources of revenue, most based on service-level agreement. This will highly contribute to the competitiveness of the EU technological market by reducing their dependency on the oligopoly of technological giants (mostly American and Chinese) that currently have a dominant position in language technologies, largely thanks to their enormous computational resources.
Projet ERC : PlayAgain (Horizon Europe, appel 2024, Proof of concept grants)
Entité participante : Université Friedrich-Alexander d'Erlangen-Nuremberg — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

We have recently identified single motor neuron activity in humans with spinal cord injury and stroke resulting in complete loss of hand function. We have then demonstrated that these individuals can control with high levels of precision the spared motor neurons ensemble in real-time, up to four degrees of freedom of the hand. We have now collected pilot data in children (<4 years old) with a completely paralyzed hand and we also found spared electromyographic activity when the children were playing with an instrumented toy. We developed a new stretchable wireless high-density EMG bracelet tailored forearm of children <3 years old, and also developed a new soft-neuroorthosis to control the small hand of the children. In this proof-of-concept project, we aim to develop a minimally invasive neuromotoric interface for children with paralyzed hands after pediatric stroke and or hemispherotomy. We will develop a system to decode the motor intention of the paralyzed children during gaming activities and associate the spared motor neuron discharge times to a wearable neuroorthosis. We have already demonstrated that our innovative neural controlled orthosis can comfortably move the hand of two paralyzed children. By leveraging the spared motor neuron output and the connection with the proposed neurorthosis, we aim to develop a new neurorehabilitation system for children, that is synchrony with the actual intended movement that are still spared after the neural lesions. This bidirectional neural interface will engage and monitor the remaining neural pathways at the cellular level, with the goal of enhancing and transforming the activity of spared motor neurons into highly functional motor capacities. By leveraging these advanced technologies, we aim to open new research and commercial avenues in paediatric neurorehabilitation and also to answer fundamental questions in movement neuroscience related to pediatric stroke or hemispherotomy.
Projet ERC : ADSOILS (Horizon Europe, appel 2023, Proof of concept grants)
Entité participante : Centre national de la recherche scientifique — Pays : France — Rôle : Partner
Acronyme de l'entité : CNRS
Type d'entité : Recherche
Financement ERC de l'entité : 49800.0 €

AdSoils project aims to evaluate, validate, and develop artificial advanced soil technology for its commercial exploitation as a tool for screening crops from belowground. As part of the Farm to Fork strategy, the European Commission aims at reduced “nutrient losses of at least 50% by 2030, while ensuring no deterioration in soil fertility. This is expected to lead to a reduction in fertiliser use of at least 20%”. The breeding and agrochemical sectors are now seeking ways for crops to acquire water and nutrient more efficiently, supplying seeds, compounds and inoculum that reduce soil contamination and make crops more resilient to climate change. Assessing the effectiveness of such strategies is a major bottleneck because current screening technologies are destructive costly and time consuming. Within the ERC consolidator grant 647857 “SENSOILs”, an artificial soil that becomes transparent during watering has been successfully developed. This technology, suited for small scale laboratory research, has shown great potential, enabling the discovery of new forms of mobility by soil bacteria and mapping root induced chemical gradients at microscale resolution. Adsoils project proposes to move a step further and to demonstrate that the AdSoils solution can be fabricated and applied in commercial screens for the discovery of agrochemicals and genomic regions involved in water and nutrient use efficiency. The project will identify cost-effective fabrication routes enabling a close mimic of natural soils and will produce a sufficient volume to validate the soil prototype with industrial end-users.
Projet ERC : PSDisrupt (Horizon Europe, appel 2025, Proof of concept grants)
Entité participante : Centre national de la recherche scientifique — Pays : France — Rôle : PI
Acronyme de l'entité : CNRS
Type d'entité : Recherche
Financement ERC de l'entité : 150000.0 €

The phylum Nucleocytoviricota includes (re-)emerging dsDNA viruses of major concern, such as monkeypox virus (mpox) and African swine fever virus (ASFV). Despite their economic and public health impact, no formally approved antivirals exist for these pathogens. Viral factories (membrane-less organelles essential for replication) are highly conserved across Nucleocytoviricota, presenting an attractive and untapped target for antiviral intervention. PSDisrupt builds on findings from the ERC-funded ViDaMa project, which identified scaffold proteins governing phase separation in viral factories. These proteins share a conserved "molecular grammar" essential for condensate formation. Disrupting this process is expected to broadly inhibit viral replication while minimizing the development of resistance. This PoC project will design phase-separation grammar-targeting antivirals using AI-driven pipelines. Deep learning models will enable the screening of ultra-large chemical spaces to identify: (i) broad-spectrum inhibitors targeting conserved scaffold proteins across Nucleocytoviricota, and/or (ii) virus-specific antivirals for mpox and ASFV. PSDisrupt’s translational goal is to design and advance lead compounds into preclinical validation and assess their commercial potential. We will: - Discover and design drugs by leveraging high throughput screens and deep learning-based algorithms. - Perform biophysical and cellular assays to validate compound efficacy. - Establish IP protection for novel inhibitors. - Engage with pharmaceutical and biotech partners for licensing and further development. By bridging virology and AI-driven drug design, PSDisrupt pioneers a novel antiviral strategy with broad applications beyond infectious diseases. The AI framework could be adapted to target phase separation in diverse pathological conditions, including cancer, neurodegeneration, and other condensate-associated diseases, significantly expanding its biomedical and commercial impact.
Projet ERC : ComPQT (Horizon Europe, appel 2025, Proof of concept grants)
Entité participante : Université Gottfried-Wilhelm-Leibniz de Hanovre — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Communications networks have brought incredible convenience to our lives, making it possible for billions of people to connect and communicate at the touch of a button. However, despite their ease of use, these networks remain vulnerable to security threats, with frequent cyber-attacks and the growing danger of quantum computing underscoring the need for stronger protections. Quantum communication offers a potential solution against these security threats. Research institutions worldwide are making significant investments in this field, with many companies commercializing relevant products. However, most of these devices predominantly use lasers as light sources, which are probabilistic by nature and can cause security vulnerability. This proposal aims to address this limitation. Compact plug-and-play quantum terminals will be built by using genuinely deterministic quantum light sources. They will serve as nodes in a clocked quantum network. Two mobile 19-inch rack-based terminals (‘transmitter’ and ‘receiver’) will be tested on a 79-kilometre quantum communication testbed that connects Hannover and Braunschweig. For demonstration purposes, one-way quantum key distribution will be realized by using these two terminals that are strictly time and frequency synchronized. This proposal builds on the successful scientific outcome from the PI’s ERC Starting Grant 'QD-NOMS', i.e., semiconductor single and entangled photon pair sources. Combined with the ultra-stable time and frequency synchronization technologies that are being developed in the current ERC Consolidator Grant 'MiNet', the full-fledged plug-and-play terminals proposed here will be instrumental in demonstrating multipartite quantum communication in a large-scale photonic network. With the Proof-of-Concept grant, we aim to narrow the gap between the lab-based quantum photonic devices and real-world applications, and finally bring ComPQT to the pre-commercial stage via market analysis and IPR activities.
Projet ERC : FORECAST-AIR (Horizon Europe, appel 2023, Proof of concept grants)
Entité participante : CERCA Institution — Pays : Espagne — Rôle : PI
Acronyme de l'entité : CERCA;I-CERCA
Type d'entité : Recherche
Financement ERC de l'entité : 150000.0 €

Air pollution represents a major global environmental health problem. 99% of the world population lives in locations where international air quality guidelines are not met. Overall, ambient air pollution causes 4.2 million premature deaths every year worldwide, half a million of which in Europe alone. Available early warning systems of air quality are generally based on location-specific thresholds of air pollutant concentrations, they are entirely based only on forecasts representing the physical processes of atmospheric chemistry, and they do not account for the inequalities in vulnerability of the exposed populations. The ERC-funded project FORECAST-AIR will go beyond these limitations in health early warning systems by integrating air quality forecasting, environmental epidemiology and the inequalities in vulnerability to implement a new method to issue public health alerts. Towards this aim, I will estimate epidemiological models between air pollution observations and health records disaggregated by causes of disease and sociodemographic vulnerable groups; I will use these models to transform bias-corrected air quality forecasts into heath predictions; I will analyse and compare the window of predictability of forecasts and predictions; and I will use this comparative study to assess the predictability of the resulting health early warning systems, so that they generate trust among public health authorities and end-users. If successful, FORECAST-AIR will drive innovation by creating operational, fit-for-purpose, early warning systems representing the health impacts of several air pollutants, with a special focus on vulnerable populations.
Projet ERC : RESPOND (Horizon Europe, appel 2024, Proof of concept grants)
Entité participante : Centre national de la recherche scientifique — Pays : France — Rôle : PI
Acronyme de l'entité : CNRS
Type d'entité : Recherche
Financement ERC de l'entité : 150000.0 €

Personalized medicine promises to exploit the diversity of human immune repertoires to design targeted T cell and B cell--based vaccines and therapeutics. However, even for mass vaccine design, identifying responding clonotypes to new viruses is costly, labor intensive and time consuming. RESPOND turns existing algorithmic solutions for identifying responding immune clonotypes into a user-friendly platform to aid drug and therapeutics discovery. RESPOND combines different algorithmic solutions and finds the best computational approach to fit the user’s needs. It is based on four methods developed during the ERC CoG STRUGGLE project: ALICE, NoiSET, fSTAR and HLA-Guessr. They are based on different properties of responding clonotypes, from sequence similarity to abundance and publicness. The idea of RESPOND is to integrate these features in a user-friendly tool that exploits the strengths of all methods to find the best answer to the practitioner’s query. RESPOND will return lists of candidates for responding clonotypes and a statistical analysis of their occurrence in databsses. Collaborating with big pharma, start-ups, medical researchers and practitioners, RESPOND will develop computational solutions to reduce the costs of biotechnological discovery and significantly decrease the time to test new vaccines and treatments. Based on consulting and feedback we will aim to give the user what they need in an appealing interface. The tool will be of use for everyone involved in exploiting immune repertoires for treatment and prophylactics, from personalized medicine, mass vaccine design to agriculture. RESPOND will pursue commercialization and market solutions both within academia, the biotechnology and medical sectors.
Projet ERC : MefiX (Horizon Europe, appel 2024, Proof of concept grants)
Entité participante : Federal Ministry for Economic Affairs and Energy — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : BMWE
Type d'entité : Recherche
Financement ERC de l'entité : 150000.0 €

"Please research electrostatic charging," pleaded the R&D leader of a major chemical company. While other ignition sources for explosions can be detected, seen, or smelled, electrostatic charge appears unexpectedly whenever liquids or solids flow. Responding to the urgent need for reliable detection of electrostatic charge, we propose advancing the MefiX flow charge measurement system, a groundbreaking innovation from the ERC project PowFEct, to the proof-of-concept stage.  MefiX represents a paradigm shift in safety management, offering the world’s first system capable of measuring turbulent flow charge in situ and with spatial resolution. Leveraging advanced machine learning techniques, MefiX identifies hidden ignition sources, providing early warnings against dangerous increases in electrostatic charge during liquid or powder transport processes. The primary technical objective of this PoC is to transition MefiX from a laboratory experiment to a functional prototype. We will validate its technical feasibility under real-world operating conditions and develop a compact, automated, cost-effective prototype device.  Our business development activities will assess the project’s commercial viability, supported by secured patents and engagement with European and international stakeholders. To attract customers and investors, we will showcase MefiX at trade fairs and establish a comprehensive roadmap for its future commercialization. Guided by an advisory board comprising multidisciplinary experts, we aim to bridge the gap between scientific excellence and commercial success, foster safe workplaces, and contribute to a prosperous society.  Given the immense value of industrial plants, it is highly profitable for companies to invest in MefiX. With quantifiable benefits regarding safety enhancement, economic savings, and potential lives saved, MefiX promises to revolutionize industrial safety protocols, ensuring a safer and more secure future for all.

Mots-clés : electrostatics explosions particles measurement
Projet ERC : DNA (Horizon Europe, appel 2025, Proof of concept grants)
Entité participante : Université technique de Munich — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

The DNA project aims to develop and deploy trustworthy distributed systems on large-scale heterogeneous datacenters (HDCs) such that their dependability properties, i.e., safety, security, and fault-tolerance, can be enforced by the foundational layers of the network hardware stack.  Distributed systems have become ubiquitous in modern cloud environments, offering high availability, perfor- mance, and resource scalability. However, ensuring the dependability and performance of distributed systems in large-scale HDCs, which are vulnerable to malicious threats and arbitrary (Byzantine) failures, is a complex task. Existing systems are limited by (a) programmability: They lack a unified, trusted foundation for security and programmability, (b) security: They have vulnerabilities and may be unable to handle malicious attacks and (c) high-performance: They can be slow, especially in complex distributed systems. These limitations force programmers to use ad-hoc methods, which can be expensive and unreliable.  The DNA project seeks to resolve the design trade-off between security and performance by introducing a new trustworthy network-level abstraction. DNA realizes this abstraction by proposing a minimalistic, high-performance, and verifiable silicon-root-of-trust as the foundational hardware building block for implementing trustworthy distributed systems.  Our implementation of DNA on commodity SmartNICs will revolutionize the way distributed systems are built in cloud environments. By providing a synergistic co-design of hardware-software networking substrate, we abstract out the hardware heterogeneity in cloud environments while providing a minimalistic interface for resource management, isolation, communication, and trust establishment. Overall, we aim to empower programmers and significantly strengthen the dependability properties of widely adopted distributed systems.