Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Error-correcting Codes and Computation (ECCC)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Computer Science and Informatics
Panel description: Informatics and information systems, computer science, scientific computing, intelligent systems
Countries involved: Israel
Call: ERC STARTING GRANTS (ERC-2022-STG, 2022)
Start date: 2023-07-01
End date: 2028-06-30
Duration (months): 60.0
Total project cost (EUR): 1489375.0
EU funding (EUR): 1489375.0
Participants: 1
Project status: SIGNED

Error-correcting codes are a method for a redundant representation of data, that enables one to can recover the original data even in the presence of some noise or corruption. In addition to their wide practical applicability, error-correcting codes are also supported by a rich theory, with connections to diverse disciplines in mathematics, science, and engineering. A particularly fruitful such connection has been the interplay with the theory of computation, where on the one hand, computational methods were used for the design of error-correcting codes admitting efficient error-correction algorithms, and in the reverse direction, such codes were useful for a variety of applications within the theory of computation. The current project aims to significantly enhance the aforementioned connections. Our first main objective is to design error-correcting codes that on the one hand, achieve the best possible information-theoretic trade-off between their redundancy and error-resilience, and on the other hand, admit super-fast error-correction algorithms. We further believe that such codes are a powerful tool that can be used for boosting the efficiency of various fundamental computational tasks, and our second main objective is to explore such potential applications. We outline a couple of such potential applications within the theory of computation, to obtaining highly-efficient proof systems, fine-grained inapproximability results, fast derandomization, and code-based cryptography with low overhead. The goals we plan to pursue are fundamental and long-standing, and even a partial progress on them would be groundbreaking, with theoretical, and potentially also practical, impact. Despite the significant challenge, there has recently been an exciting progress towards these goals (including by the PI), and we consequently believe that we are now in a unique position for pursuing these goals.

Keywords: error correcting codes,coding theory,theory of computation,complexity theory
Engineering of bacteria to see light (EOS)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting 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 STARTING GRANTS (ERC-2023-STG, 2023)
Start date: 2024-01-01
End date: 2028-12-31
Duration (months): 60.0
Total project cost (EUR): 1500000.0
EU funding (EUR): 1500000.0
Participants: 1
Project status: SIGNED

EOS wants to create a new class of biohybrid living materials capable to perceive light and perform tasks remotely (i.e. drug delivery in hard-to-reach body locations). I propose to achieve this by inducing light sensitivity into non-photosynthetic motile bacteria at minimum invasiveness and complexity. Based on the notion that the active manipulation of the bacteria membrane potential allows controlling bacterial functions, including motion, EOS challenge is how to trigger effectively a membrane potential dynamics. EOS will employ a materials-based approach that I call “optobacterial-stimulation” and is composed by two key ingredients: i. light as a spatiotemporal precise tool that can control bacteria remotely; ii. phototransducing organic materials that associate spontaneously with bacterial cells without the need for neither covalent attachment nor genetic modification. The phototransducing mechanism stems from the “cross-talking” between molecular excitations and the polarization state of cells. The photoinduced membrane potential dynamics is ultimately linked to ion motive force, and thus to the bacterial flagellar motor. All these abovementioned elements have in common the study of light-matter interaction, which represents my main research interest in the last decade. Using optobacterial-stimulation I will answer to three outstanding questions that are related to each other and shape EOS objectives: i. can we engineer bacteria to perceive light through exogenous phototransducers? ii. are bacteria able to perceive different photostimulation approaches, as dictated by molecular excited states, in terms of membrane potential dynamics and motion? iii. can we understand the relationship between the fate of excited states and bacterial function? In the case study, I will demonstrate phototactic guidance of “eyeless” bacteria that are competent to swim in the gastrointestinal tract, with the view to develop intrinsically bio-compatible microswimmers.

Keywords: biohybrid living materials,organic phototransducers,bacteria photostimulation,photodriven bacteria microswimmers
A Digitally-Enabled Electroconductive Patient-Specific Stimulation Implant for Spinal Cord Injury (DEEPSTIM)

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: Ireland
Call: ERC PROOF OF CONCEPT GRANTS (ERC-2024-POC, 2024)
Start date: 2025-07-01
End date: 2026-12-31
Duration (months): 18.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: SIGNED

Spinal cord injury (SCI) can severely impact patients, impairing neurological function. The complex pathophysiology of SCI and the poor growth capacity of neurons results in a multi-faceted challenge to repair. Lifetime treatment costs for SCI patients can be as high as €2 million and approximately 11,000 new spinal cord injuries occur every year in the EU. Neuromodulation devices that provide controlled electrical stimuli to enable pain management, aid rehabilitation and represent a growing medical device market, predicted to reach an €83 Bn value by 2030. This project proposes to combine electrical stimulation for the integration of neuromodulatory functionality with a tailored tissue engineered device to enhance regrowth of injured neurons and restore nerve function. It builds on the cutting-edge 3D-printing technologies developed during my ReCaP Advanced Grant in order to design a ground-breaking patient-specific implant for the targeted delivery of electrical stimulation to promote SCI repair. High-resolution imaging of individual patient spinal cord injuries will facilitate 3D printing of an electroconductive implant architecture, filled with a biomimetic regenerative and immunomodulatory biomaterial, that matches both the gross lesion geometry as well as mimicking key aspects of the injured spinal axonal tract anatomy. The 3D-printed conductive architecture will be geometrically optimized to distribute externally delivered electrical stimulation to neurons throughout the injury – maximizing the therapeutic benefits of electrical stimulation. To demonstrate proof-of-concept, DEEPSTIM will develop a prototype for preclinical testing in a rat model of SCI. The DEEPSTIM team will work with institutional commercialisation teams and seek input from clinical experts to increase the technological readiness level of the platform with the ultimate aim of forming a spin-out company to further develop the technology towards clinical translation.
A Comprehensive Framework enabling the Delivery of Trustworthy Datasets for Efficient AIoT Operation (PANDORA)

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
Partnership: AI-DATA-ROBOTICS (co-programmed)
Countries involved: Greece,Denmark,Sweden,France,Germany,Netherlands,Slovakia,Cyprus,Ireland,Italy,Serbia,India,Canada,Switzerland
Call: Efficient trustworthy AI - making the best of data (AI, Data and Robotics Partnership) (RIA) (HORIZON-CL4-2023-HUMAN-01-CNECT, 2023)
Start date: 2024-04-01
End date: 2027-03-31
Duration (months): 36.0
Total project cost (EUR): 8991728.75
EU funding (EUR): 8991728.75
Participants: 27
Project status: SIGNED

As Internet of Things (IoT) and IoT-Edge-Cloud continuum technologies advance, physical environments are becoming increasingly equipped with sensors, fuelling the development of smart space ecosystems. Massive quantities of data produced by IoT devices revolutionize the way such ecosystems operate via the exploitation of AI models/services. This has led to the emergence of the so-called Artificial Intelligence of Things (AIoT) systems. In general, designing techniques to promote robustness, efficiency and continual operation of AIoT systems requires realistic and trustworthy data at scale. However, such data is not always easy to obtain due to the cost of smart space construction, the inconvenience of long-term device tracking, the sensor/knowledge data gaps in diverse scenarios of a smart space, and the restrictions imposed on sensitive data sharing. Furthermore, an efficient AIoT system operation requires trustworthy AI services, as well as novel approaches for speeding up their inference across the IoT-Edge/Cloud continuum. PANDORA aims to devise and implement a comprehensive framework enabling the delivery of trustworthy datasets of smart space ecosystems, as well as the deployment and green operation of AIoT systems in such spaces. PANDORA spans two phases: (1) prior to AIoT system deployment; (2) post AIoT system deployment and operation. Phase 1 proposes and combines a series of novel techniques such as synthetic data generation, quantification of uncertainties, and data summarization for the delivery of trustworthy datasets, as well as explainable AI and domain-informed model training/testing in smart space ecosystems. Phase 2 defines novel AIaaS and CaaS techniques for the robust, explainable, green and continual operation of AIoT systems deployed in such spaces. The trustworthiness and applicability of the PANDORA framework will be tested through five pilot cases hosting AIoT applications in smart buildings, factories and critical infrastructures.

Keywords: aiot systems,synthetic data generation,data driven ai,green computing,trustworthy ai,explainable ai,iot edge cloud continuum,domain knowledge modeling,continual learning
Novel SOE architectures for hydrogen production (NOAH2)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Climate, Energy and Mobility
Theme: Public-private partnerships
Destination: Clean Hydrogen
Action type: Research and Innovation actions
Partnership: CLEANH2 (JU-JTI)
Countries involved: Denmark,France,Greece,Norway,Switzerland,United Kingdom
Call: Innovative Solid Oxide electrolysis cells for intermediate temperature hydrogen production (HORIZON-JTI-CLEANH2-2023-1, 2023)
Start date: 2024-01-01
End date: 2026-12-31
Duration (months): 36.0
Total project cost (EUR): 2656024.0
EU funding (EUR): 2655084.0
Participants: 7
Project status: SIGNED

Hydrogen is a key energy vector in a future decarbonised economy. Large-scale application in numerous sectors, such as transport, iron & steel plants, and the chemical industry, requires efficient and sustainable production routes of green hydrogen. Electrolysis of water/steam using electricity from renewable sources like wind and solar is the solution. High temperature or solid oxide electrolysis (SOEL) has significantly attractive features, which allow for lower CAPEX and OPEX, thus facilitating commercial breakthrough: High electrical efficiencies approaching 100%, cost competitive, non-noble materials, and operational flexibility. SOEL challenges that need to be solved are increase of lifetime and reduction of degradation for realistic applications, the ceramic brittleness of most mature SOEL configurations, which challenge rapid operational strategies when integrated with renewable energy sources, and scaling costs for the required Mega to Gigawatt volumes. NOAH2 aims at overcoming these challenges. The overall goal of the NOAH2 project is to provide a robust, cost-competitive, flexible, and durable stack concept for hydrogen production at intermediate temperatures through innovative electrode, cell, and stack designs. NOAH2 will boost the electrolysis performance of solid oxide cells & stacks significantly beyond State-of-the-Art (SoA) through a combination of optimised structures and highly active materials, with a focus on reducing critical raw materials (CRM) and manufacturability using well-established large scale routes for solid oxide technology. The NOAH2 stack architecture relies on a metal based monolithic concept with infiltrated electrodes. NOAH2 will outline a path towards commercialisation, provide a sustainability classification with emphasis on substituting CRM, provide an assessment of commercialization potential compared to SoA SOEL, PEM, and Alkaline electrolysers, and identify potential industrial players for high-volume manufacture.

Keywords: soel,monolith,metal based
One Health approach to understand, predict and prevent viral emergencies from bats (OneBAT)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Health
Theme: Health
Destination: Tackling diseases and reducing disease burden
Action type: Research and Innovation actions
Countries involved: Italy,France,United Kingdom,Hungary,Belgium,Spain,Cyprus,United States,Ukraine
Call: Pandemic preparedness (HORIZON-HLTH-2022-DISEASE-07, 2022)
Start date: 2023-12-01
End date: 2026-11-30
Duration (months): 36.0
Total project cost (EUR): 2128100.0
EU funding (EUR): 2122287.5
Participants: 14
Project status: SIGNED

In recent years, several novel RNA viruses found in bats have been associated to the emergence of human diseases. Surveillance in this extremely diverse group of mammals has revealed that bats are significant reservoir hosts for many viruses whose zoonotic potential deserves further insights. OneBAT aims at investigating the trigger for viral spillover from European bats to humans or domestic animals, using a multidisciplinary consortium of ecologists, virologists and modellers. In response to the outcomes foreseen by the call, OneBAT will unravel the complex interplay between natural and spillover host, pathogen and environment that determines the emergence of infectious diseases. Information on viral replication pathways, antigenic markers, and in vitro and in vivo screening of antivirals will advance the availability of current and future therapeutic and prophylactic tools for Disease X. Due to technical and logistical reasons, OneBAT will not investigate all the bat species and pathogens circulating in Europe; it will rather provide well-based evidence on relevant model species that could be used to study other bat species or viruses that already are or may become of interest over the lifetime of the project or in the future. We will focus on the Miniopterus schreibersii bats, a species in significant decline across Europe, and on filoviruses, coronaviruses and lyssaviruses, as high-consequence pathogens known to circulate in the target bat. In addition to the specific information stemming from the project, OneBAT will establish a methodological platform that will make available the developed ecological and virological tools. Among them, innovative protocols for serological and virological investigations of these small-seize animals; harmonised longitudinal surveys across Europe, including tracking systems. Ultimately, OneBAT will equip southern and eastern EU countries with 20 VHF receivers that could be used for further studies on all flying animals.

Keywords: chiroptera,climate change,wildlife movement tracking systems,spillover,high consequence rna viruses,bat human interface,innovative diagnostic and research,prevention,therapy,stakeholder tool
Thermography AI - cornerstone in future preventive healthCARE (AI-CARE)

Framework: Horizon Europe — Innovative Europe
Programme: European innovation ecosystems
Theme: Elevating the scalability potential of European business
Destination: Elevating the scalability potential of European business
Action type: Coordination and support actions
Countries involved: Bulgaria
Call: Women TechEU (HORIZON-EIE-2022-SCALEUP-02, 2022)
Start date: 2023-07-01
End date: 2024-02-29
Duration (months): 8.0
Total project cost (EUR): 107142.86
EU funding (EUR): 75000.0
Participants: 1
Project status: CLOSED

It is estimated that more than 230M people worldwide suffer from Peripheral Arterial Disease (PAD) and each year more than 22M of them develop Critical Limb Ischemia - a life-threatening condition with 50% combined incidence of amputation and/or death in a 3-year period. Timely PAD diagnosis is the most important factor to avoid complications by treatment procedures (revascularization) and proper disease management (daily walks, change of dietary habits, etc.). Unfortunately, half of the PAD-suffering population is asymptomatic and therefore lately diagnosed. Recent technology advancements in Thermography (portable, high-resolution and precise hardware) and Artificial Intelligence (scalable infrastructure and highly accurate computer vision analysis) can support much needed progress towards continuous and preventive and value-based healthcare. Kelvin Health develops a clinical decision support system based on Thermography AI for non-invasive, cost-efficient population-wide screening and diagnosis, initially addressing pathology related to PAD - blockage or narrowing of limbs’ blood vessels. The system applies a portable thermal imaging camera that captures body thermodynamics and generates a series of thermograms, which are then thermally segmented, and analyzed temporarily using AI image recognition algorithms. The ML model is trained to detect anomalies related to the vascular system using state-of-the-art machine learning algorithms such as deep neural networks and, in particular convolutional neural networks. AI-CARE proposal aims at addressing the challenges we meet to structure clear regulatory pathway for market approval at initial target markets, design a clinical validation study and assess the patentability of our method. At the same time promoting women entrepreneurship, especially in deep tech healthcare where women are highly underrepresented. Successful project implementation will lead to Kelvin Health's success in R&D capital fundraising.

Keywords: thermography ai,
mInimize coSts and mAximize Benefits in the transitionary European regional Labour markets (ISABEL)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Culture, creativity and inclusive society
Theme: Culture, creativity and inclusive society
Destination: Innovative Research on Social and Economic Transformations
Action type: Research and Innovation actions
Countries involved: Greece,Denmark,Poland,United Kingdom,Spain,Serbia
Call: Minimise costs and maximise benefits of job creation and job destruction (HORIZON-CL2-2024-TRANSFORMATIONS-01, 2024)
Start date: 2025-01-01
End date: 2027-12-31
Duration (months): 36.0
Total project cost (EUR): 2985812.5
EU funding (EUR): 2985812.5
Participants: 10
Project status: SIGNED

It is evident that the process of green transition reshapes national, regional, and local economic landscapes, and triggers change in ecosystems of various industries, thereby highlighting the urgent need to plan efficient and fair strategies to minimise the costs of job destruction and maximise the benefits of job creation in a socially fair way and a geographically equal way, in the wider patterns of reskilling. ISABEL's main objective is to provide a threefold solution to the aforementioned problem related to JCD with the use of new technologies such as AI and Large Language Models (LLM) that will 1) enrich our understanding of the socially and geographically uneven implications of this process across Europe, 2) broaden our knowledge on the factors underlying this process, and 3) highlight pathways of minimising the effects of job destruction and maximising the benefits of job creation, in a socially and spatially fair way. The latter include upskilling and reskilling of workers and the reallocation of labour, based on existing and forecasted skills shortages, using AI technologies to support these tasks. To achieve its main goal, ISABEL will examine the above elements at 3 different levels: i) the aggregate European level, among the EU, UK and Serbian Nomenclature of Territorial Units for Statistics 2 regions (NUTS2), using secondary data, ii) at 6 countries of focus -Denmark, Greece, Poland, UK, Serbia, Spain, collecting and analysing primary data, and iii) Living Labs focusing on 6 regions with diverse socio-economic features.

Keywords: regional labour market restructuring work transformation skills shortages green transition
What Makes People Targets: A Multi-Actor Study of How Ethnic Discrimination is Perceived, Tackled and Avoided (TARGETS)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: The Soci al World and Its Interactions
Panel description: Sociology, social psychology, education  sciences , communication  studies
Countries involved: Italy
Call: ERC STARTING GRANTS (ERC-2021-STG, 2021)
Start date: 2022-09-01
End date: 2027-08-31
Duration (months): 60.0
Total project cost (EUR): 1499290.0
EU funding (EUR): 1499290.0
Participants: 2
Project status: SIGNED

Despite progressive anti-discrimination legislation and the popularity of diversity policies touted as the key to a culturally diverse workplace, discrimination of ethnic minorities in the labor market is remarkably persistent and pervasive. TARGETS integrates literature from sociology, social psychology, organization and sociolegal studies into a novel multi-actor and dynamic theoretical framework to examine what makes people targets of ethnic discrimination. Both conceptually and empirically, the key contribution of our approach is that we define and operationalize discrimination claims as, inherently, relational. We test the core proposition that relational claims-making is the key mechanism through which discrimination is perceived, legitimated or contested by multiple actors. We develop and test this theory at two levels of analysis. At the macro level, we map how structures and practices, such as anti-discrimination laws and diversity management policies, can confer or deny legitimacy to discrimination claims, in the workplace and courtroom. These structures may create an 'illusion of fairness' that reproduce durable inequalities in the distribution of organizational resources, such as access to jobs and career opportunities. We then zoom in on the micro-foundations of the claims-making process, focusing on its relational and dynamic nature, respectively. First, using an innovative research design, we pioneer the use of linked, multi-actor factorial survey experiments to capture the discrimination attributions made by multiple actors (targets, perpetrators, allies and bystanders), simultaneously. Second, we collect real-time longitudinal data on the job search strategies that ethnic minorities adopt to avoid becoming targets. Our dynamic approach improves on existing accounts that all too often treat them as passive labor market agents, and contributes to theory development on how supply-side behavior can counteract labor market inequalities.

Keywords: employers,hiring and organizations factorial surveys ethnic inequality labour markets