Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Boosting Resilience In five industrial ecosystems through adoption and Deployment of advanced technoloGiEs among SMEs (BRIDGESMEs)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Digital, Industry and Space
Theme: Industry
Destination: Increased Autonomy in Key Strategic Value Chains for Resilient Industry
Action type: Coordination and support actions
Countries involved: Italy,Czechia,Spain,France,Greece,Austria
Call: Boosting generation and diffusion of advanced technologies in SMEs based on a supply chain model (CSA) (HORIZON-CL4-2023-RESILIENCE-01, 2023)
Start date: 2023-12-01
End date: 2026-11-30
Duration (months): 36.0
Total project cost (EUR): 2746241.25
EU funding (EUR): 2487491.25
Participants: 9
Project status: SIGNED

The European Commission introduced the concept of "Industry 5.0" in 2021, shifting from the technology-driven approach of Industry 4.0 to a human-centric approach that prioritizes human needs and interests in production processes. The need for this new paradigm was emphasized by major disruptions such as the COVID-19 pandemic and ongoing conflicts, which revealed low resilience in industrial ecosystems in Europe. Small and medium-sized enterprises (SMEs) are facing challenges such as climate change, resource scarcity, demographic changes, and skills gaps that further disrupt their operations. The aftermath of the pandemic has forced businesses to re-think their operations and adapt their business models to ensure continuity. In this context, the BRIDGESMEs project aims to assist European SMEs in five industrial ecosystems - Mobility, Transport & Automotive; Aerospace & Defence; Electronics; Cultural & Creative Industries; and Tourism - in re-thinking their business models and operations to become more resilient, sustainable, and human-centric. The project will facilitate alliances between traditional SMEs and tech-savvy SMEs & start-ups, as well as accelerate the deployment of innovative solutions in the market and enhance clusters' cooperation to support SMEs across value chains. The consortium will conduct an assessment of the current status of the Five Industrial Ecosystems, identify needs, disruptions, challenges, and opportunities related to technology adoption and transition towards Industry 5.0, and involve and select traditional and tech-savvy SMEs through open calls, hackathons, and matchmaking events. Cluster action for industrial transition, CMO guidelines, and policy recommendations will be delivered through peer review meetings to promote increased cooperation between clusters and enhance resilience and technological uptake across value chains.

Keywords: industry 5 0,tech savvy smes,traditional sme,advanced technologies,innovation,mobility,transport and automotive,aerospace and defence,electronics,cultural and creative industries,tourism
Towards the leading constant in Stacky Manin’s conjecture (Stacky Manin)

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: Mathematics
Countries involved: Türkiye
Call: MSCA Postdoctoral Fellowships 2023 (HORIZON-MSCA-2023-PF-01, 2023)
Start date: 2024-09-16
End date: 2026-09-15
Duration (months): 24.0
Total project cost (EUR): 148478.4
EU funding (EUR): 148478.4
Participants: 1
Project status: SIGNED

Abstract: Manin’s conjecture is a famous conjecture in arithmetic geometry, predicting asymptotically the number of rational points of bounded height on Fano varieties. In a breaking recent advance, the researcher and Yasuda have [Darda-Yasuda]extended it to stacks, which explains Malle’s conjecture on the number of Galois extensions with fixed Galois group of bounded discriminant. The leading constant in the asymptotic formula, however, remains unknown. A reason for this is that we still have very little verified examples of Stacky Manin’s conjecture. In this proposal, we identify a class of stacks for which the conjecture seems to be approachable, namely, the class of smooth complete intersections in weighted projective stacks which enjoys rich geometric properties. We then plan to analyze the obtained constant, compare it with existing examples, to guess the shape in the general case.

Keywords: manin s conjecture,malle s conjecture,rational points,peyre s constant
Society-Aware Machine Learning: The paradigm shift demanded by society to trust machine learning. (SAML)

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: Germany
Call: ERC STARTING GRANTS (ERC-2021-STG, 2021)
Start date: 2023-02-01
End date: 2028-01-31
Duration (months): 60.0
Total project cost (EUR): 1499845.0
EU funding (EUR): 1499845.0
Participants: 1
Project status: SIGNED

To date, the design of ethical machine learning (ML) algorithms has been dominated by technology owners and remains broadly criticized for strategically seeking to avoid legally enforceable restrictions. In order to foster trust in ML technologies, society demands technology designers to deeply engage all relevant stakeholders in the ML development. This ERC project aims at responding to this call with a society-aware approach to ML (SAML). My goal is to enable the collaborative design of ML algorithms, so that they are not only driven by economic interests of the technology owners but are agreed upon by all stakeholders, and ultimately, trusted by society. To this end, I aim to develop multi-party ML algorithms that explicitly account for the goals of different stakeholders---i.e., owners, those experts that design the algorithm (e.g., technology companies); consumers, those that are affected by the algorithm (e.g., users); and regulators, those experts that set the regulatory framework for their use (e.g., policy makers). The proposed methodology will enable quantifying and jointly optimizing the business goals of the owners (e.g., profit); the benefits of the consumers (e.g., information access); and the risks defined by the regulators (e.g., societal polarization). The SAML project involves a high-risk/high-gain paradigm shift from an owner-centered to a society-centered (multi-party) ML design. On the one hand, it will require significant and challenging methodological innovations at every stage of the ML development: from the data collection all the way to the algorithm learning. On the other hand, it will impact how ML technologies are deployed in society by enabling an informed discussion among different stakeholders and, in general, by society about these new technologies. The results of this project will provide the urgently needed methodological foundations to ensure that these new technologies are at the service of society.

Keywords: machine learning,ethical and trustworthy machine learning,fairness and interpretability of machine learning,societal impact of machine learning,fairness in algorithmic decision making,societal polarization in algorithmic recommendations on online social media
Fortifying the automated smartphone-based Cough Audio classification for rapid triaGE testing for tuberculosis project (4-CAGE-TB)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Health
Theme: Public-private partnerships
Destination: Global Health EDCTP3
Action type: Research and Innovation actions
Partnership: EDCTP3 (JU-JTI)
Countries involved: South Africa,Netherlands,Uganda,Germany
Call: Funding to successfully finalise EDCTP2-funded clinical trials that were negatively impacted by the COVID-19 pandemic (HORIZON-JU-GH-EDCTP3-2023-01, 2023)
Start date: 2025-01-01
End date: 2026-12-31
Duration (months): 24.0
Total project cost (EUR): 981469.25
EU funding (EUR): 981469.25
Participants: 4
Project status: SIGNED

Tuberculosis (TB) is the single biggest infectious cause of death globally, a situation worsened by COVID-19. Revolutionary new TB triage tests are required, including at facilities where people are accessible and efficiently referable to confirmatory testing. The Cough Audio triaGE for TB (CAGE-TB) study was EDCTP2-funded to 1) collect cough sounds at people’s point-of-entry into primary care facilities (South Africa) and derive an audio classifier, 2) validate diagnostic accuracy in independent cohorts (South Africa, Uganda), and 3) deploy mixed methods research (costing, implementation science, medical anthropology) to inform design and implementation so that this classifier, which will report people as “likely TB” or “unlikely TB” for confirmatory testing, is embedded within a user-friendly mHealth app with on-device offline computation. Per the original call’s objective, CAGE-TB’s goal was to deliver an accurate validated mHealth app usable in trials assessing clinical outcomes (necessary for adoption). Uniquely, this pure mHealth innovation mitigates barriers that jeopardise target product profile criteria (e.g., reagents, cold-chain, transport, infrastructure). After CAGE-TB hired personnel, COVID-19 prevented and slowed recruitment (people had limited clinic access, recruitment interrupted by successive COVID-19 waves). Throughout this, CAGE-TB paid personnel and trainees, resulting in severe budget overruns without participant recruitment, and limited trainee progress and site visits. In 4-CAGE-TB we request essential support over two years to accomplish the original scope-of-work, ensure trainees can finish degrees and, due to the longer recruitment period, accommodate critical additional site visits. COVID-19 has only reinforced our premise: it caused TB to increase for the first time in a decade, damaged already weak facility-based triage practices, and accelerated cough classification technologies.

Keywords: tuberculosis,covid 19,diagnostics,capacity development,triage,mhealth,machine learning
Strong-coupling-enhanced nanoparticle array organic light emitting diode (SCOLED)

Framework: Horizon Europe — Innovative Europe
Programme: The European Innovation Council (EIC)
Theme: EIC Pathfinder
Destination: Open to supporting projects in all areas of science, technology,...
Action type: EIC actions
Countries involved: Finland,Netherlands,United Kingdom,Spain,Lithuania
Call: EIC Pathfinder Open 2022 (HORIZON-EIC-2022-PATHFINDEROPEN-01, 2022)
Start date: 2023-05-01
End date: 2026-04-30
Duration (months): 36.0
Total project cost (EUR): 2728447.0
EU funding (EUR): 2728446.75
Participants: 6
Project status: SIGNED

We propose a radical new solution to the problem of increasing the efficiency of organic light-emitting diodes (OLEDs) based on modifying the light-matter coupling by nanostructures. All previous attempts to increase the efficiency of OLEDs to be competitive with commercial inorganic LEDs have failed. If successful, our new vision for strongly coupled organic light-emitting diodes (SCOLEDs) will bypass the existing technological bottleneck. OLEDs can be fabricated from earth-abundant non-toxic materials using energy-efficient processes, in stark contrast to the present market-leading inorganic LEDs. However, despite their much lower environmental impact, the widespread deployment of OLEDs has been blocked by their limited efficiency. To achieve the required step-change in efficiency, plasmonic nano-particle arrays will be used to enhance the coupling between light and matter within OLEDs. Our objectives are to enhance OLED efficiency to a level competitive with inorganic LEDs, and at the same time to adjust the periodicity, size and shape of the nanoparticles to control the color, polarization and directional distribution of the emitted light. Analytic theory, numerical simulations and nanofabrication will be combined with optical and electronic characterization across an interdisciplinary team with expertise ranging from materials science and electronics to photonics and quantum physics, including world-leading proficiency in nanoparticle arrays and strong light-matter coupling. Our ambitious target is the proof-of-principle demonstration of an OLED with more than 50% external quantum efficiency and tailorable control of the properties of the emitted light. SCOLEDs offer the prospect of a breakthrough technology that will dramatically reduce the environmental impact of LED technology in lighting and display applications, and will widen the palette of OLED applications to new and emerging areas such as electronic vehicles, augmented reality and urban agriculture.

Keywords: organic light emitting diodes oleds,nanoplasmonics,nanophotonics,nanoparticle arrays,strong light matter coupling,exciton polaritons,light management,spectral shaping
Probing cosmic large-scale structure beyond the average (LSS_BeyondAverage)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Universe Sciences
Panel description: Astro -physics/ -chemistry/ -biology; solar system; planetary systems; stellar, galactic and extragalactic astronomy; cosmology; space sciences; astronomical instrumentation and data
Countries involved: Germany
Call: ERC STARTING GRANTS (ERC-2022-STG, 2022)
Start date: 2024-03-01
End date: 2029-02-28
Duration (months): 60.0
Total project cost (EUR): 1496955.0
EU funding (EUR): 1496955.0
Participants: 1
Project status: SIGNED

The cosmic large-scale structure is entering a new ‘full space’ era with the launch of the Euclid galaxy survey satellite in 2023 coinciding with the first light for the Rubin Observatory. My proposal is centred on using galaxy surveys across a third of the sky as a laboratory for fundamental physics beyond the standard model by combining powerful theoretical models with exquisite data for galaxy clustering and weak gravitational lensing. To fully realise the potential of Euclid’s ‘full space’ galaxy survey, I will use my unique expertise in developing analytical, computational and statistical methods to predict and analyse the cosmic large-scale structure beyond the average. I will hunt for fundamental physics by going beyond the average probed by standard two-point analyses and unlocking additional information from novel clustering statistics sensitive to different density environments. I will provide new insights into dark matter dynamics and enable a field-based analysis by pioneering a particle-wave duality inspired phase-space technique that goes beyond the average effective fluid modelling. This will push forward modelling to a new level, overcome limitations of standard fluid and N-body methods, and enable the joint modelling of dark matter, baryons and massive neutrinos. My research program will unlock key insights into particle physics (by pinning down the total neutrino mass and potential wavelike properties of dark matter), the early universe (by constraining primordial non-Gaussianity providing hints for inflationary physics), gravity (by measuring the nature of dark energy and potential modifications of general relativity) and astrophysics (by determining the relationship between galaxies and dark matter). My expertise in developing analytical, computational and statistical methods for clustering dynamics and statistics and my leadership within the Euclid collaboration places me in a strategic position to lead such an ambitious research program.

Keywords: late time cosmology,dark matter,gravitational clustering,galaxy surveys,galaxy clustering statistics,weak gravitational lensing statistics,cosmic structure formation,large scale structure,dark energy,modified gravity,massive neutrinos,primordial non gaussianity
A European-wide foundation to accelerate Data-driven Cancer Research (EOSC4Cancer)

Framework: Horizon Europe — Excellent Science
Programme: Research infrastructures
Theme: Research infrastructures
Destination: EOSC ecosystem
Action type: Research and Innovation actions
Partnership: EOSC (co-programmed)
Countries involved: Spain,Greece,Italy,Germany,Netherlands,United Kingdom,Czechia,Austria,France,Finland,Sweden,Norway,Belgium
Call: FAIR and open data sharing in support of cancer research (HORIZON-INFRA-2021-EOSC-01, 2021)
Start date: 2022-09-01
End date: 2025-05-31
Duration (months): 33.0
Total project cost (EUR): 7814549.51
EU funding (EUR): 7814549.0
Participants: 38
Project status: SIGNED

Cancer complex nature requires integration of advanced research data across national boundaries to enable progress. Indeed, the Horizon Europe mission board for cancer has identified access to data, knowledge and digital services - accessible across the European Research Area through federated infrastructures - as a key enabling condition for success. The better we organise cancer data across Europe, the better and faster we can bring the fruits of new biological and technical innovations to the benefit of EU citizens/patients. EOSC4Cancer will make cancer genomics, imaging, medical, clinical, environmental and socio-economics data accessible, using and enhancing existing federated and interoperable systems for securely identifying, sharing, processing and reusing FAIR cancer data across borders, and it will offer them via community-driven analysis environments. EOSC4Cancer provision of well curated datasets will be essential for advanced analytics and computational methods to be reproducible and robust, including machine learning and artificial intelligence approaches. EOSC4Cancer use-cases will cover the patient journey from cancer prevention to diagnosis to treatment, laying the foundation of data trajectories and workflows for future cancer mission projects. EOSC4Cancer brings together a comprehensive consortium of cancer research centres, research infrastructures, leading research groups, hospitals and supercomputing centres from 14 European countries. To make the developments sustainable, these will be offered as part of the research infrastructures partners services portfolio, in connection with the EOSC ecosystem and to serve the European Cancer Mission, which will be possible via the engagement with large international coalitions, e.g. ICGC-Argo, GA4GH, 1+MG/B1MG, Cancer Core Europe, European Cancer Information System, European Network of Cancer Registries, Innovative Partnership for Action Against Cancer Joint Action and patients/survivors associations.

Keywords: eosc,cancer research,research infrastructures,patients,data governance,health data space,fair data,distributed computing
Recovering plasma-facing components temperatures in fusion devices from IR camera measurements (MAGRITTE)

Framework: Horizon Europe — Widening Participation and Strengthening the European Research Area
Programme: Widening participation and spreading excellence
Theme: Attracting and mobilising the best talents
Destination: Attracting and mobilising the best talents
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: Slovenia,France
Call: Fostering balanced brain circulation – ERA Fellowships (HORIZON-WIDERA-2022-TALENTS-04, 2022)
Start date: 2024-09-01
End date: 2026-08-31
Duration (months): 24.0
Total project cost (EUR): 171399.36
EU funding (EUR): 171399.36
Participants: 2
Project status: CLOSED

Infrared (IR) cameras are key diagnostics to monitor and control plasma-facing components (PFCs) in fusion devices. Nevertheless, the use of all-metallic PFCs with low and variable emissivity (ε ~ 0.1-0.5) makes it difficult to obtain a correct surface temperature measurement. The radiance collected by the IR camera includes both the thermal radiation emitted by the target and parasitic radiation coming from the target's surroundings. Furthermore, target emissivity changes with the surface temperature and roughness. This causes significant errors in the surface temperature measurement that we need to address for achieving high power and safe plasma operation. Inaccurate interpretation of IR temperature measurement could endanger machine safety (temperature underestimation) or on the contrary, lead to unnecessary pulse interruptions that reduce the overall performance of the machine (temperature overestimation). The current approach is to convert the radiance collected by each pixel in an apparent using a physical relationship between emitted radiance and temperature (Planck’s law). Because a portion of the collected radiance is due to reflection on the target, a systematic error is made. This method is unable to recover the portion of reflected radiance and to deduce the correct temperature of the target using only the emitted radiance. The proposed technique in this project is to use a digital twin of the machine and to make in it assumptions on the temperatures of the different elements of the machine in order to simulate the images that would be measured under these supposed conditions. By comparing to the real image measured by the camera and using optimization techniques, the assumptions on the parameters can be updated until the simulated and measured images are close enough. It is then deduced that the assumptions made on the parameters of the digital twin are correct and that they correspond to the real temperatures in the machine.

Keywords: fusion,plasma facing components,infrared camera,temperature measurement,inverse methods
Developing an Abductive Coding Analysis Toolkit (DACAT)

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: Belgium
Call: ERC PROOF OF CONCEPT GRANTS (ERC-2023-POC, 2023)
Start date: 2024-03-01
End date: 2025-08-31
Duration (months): 18.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: CLOSED

In qualitative data analysis oriented towards theory-building, many data analysts anchor their methodological approach – explicitly or not – in grounded theory. However, as research in teams develops, handling larger amounts of qualitative data enabled by technological developments, grounded theory’s principles are increasingly at odds with current practices; and analysts of qualitative data are left relying on coding procedures that do not match their realities. Following a burgeoning literature and building on the ERC-funded QUALIDEM project, we argue that shifting to an abductive approach of coding, that combines deduction and induction at different stages of data analysis, provides an epistemology to qualitative research that aims at theory-building. This PoC aims to develop abduction as a method of qualitative data analysis by providing a toolkit for abductive coding – the Abductive Coding Analysis Toolkit (ACAToolkit). The ACAToolkit includes three main components: (1) a practical guide ; (2) an educational program for training; (3) an overview of the potentials and limits of existing QDA software - for abductive coding. The toolkit, thereby, targets several audiences: academics, instructors, data analysts and software developers. Our pathway from research to innovation consists of three steps: (1) mapping the existing practices, and tools of abductive coding in different disciplines through a systematic literature review whose results will be presented in a technical report; (2) developing our abductive coding analysis toolkit through research collaborations and consultancy agreements with stakeholders – educators, software developers and data analysts, synthesized during two cocreation workshops; (3) testing the ACAToolkit with different potential end-users during five in situ tests – with researchers, students, educators, software developers, and data analysts. The toolkit aims to be free, attractive, widely applicable, and with a wide disciplinary range.