Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Multilingual Assets and Resources for Modular Translation (MARMoT)

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: Finland
Call: ERC PROOF OF CONCEPT GRANTS (ERC-2024-POC, 2024)
Start date: 2025-06-01
End date: 2026-11-30
Duration (months): 18.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: SIGNED

Recent innovations in language technologies have introduced large language models that industry players often pitch as one-size-fit-all solutions. These systems are however rarely reusable in novel contexts, nor sustainable given their massive parameter counts, nor scalable across the thousands of languages spoken in the world. In response, we intend to build upon the findings of ERC funded project FoTran, and develop modular architectures in which parts of a network are designed to be specialized for a given task, and specialized components can be added throughout the lifetime of a model. This approach has the potential to significantly reduce the growing carbon footprint of generative AI, and will allow us to reach a broad language coverage and target under-served languages. Our MARMoT projects focuses more specifically on two goals: (i) converting our research prototypes of scalable modular architectures for language modeling and translation into production-ready software with broad applicability and (ii) developing pretrained models that can readily be used and adapted to a wide range of languages, scenarios and real-world applications. We will emphasize the support of under-resourced languages and domains in order to push for inclusiveness and language service equality. This PoC will focus on developing open tools that can be used not only in computer-assisted translation, but also in related language services such as summarization, paraphrasing and text simplification. We will produce customizable language processing assets based on a modular design that will put the emphasis on scalability, reusability, and sustainability, thereby making them efficient during inference and easily deployable by key stakeholders, from industry to government agencies. This platform will deliver language technology solutions that can be customized at the task level so as to fit a broad array of language and application contexts.

Keywords: natural language processing,nlp,machine translation,language models,artificial intelligence,ai,language technology,neural machine translation,multilingual nlp
Delivering Renewal and Innovation to mass Vehicle Electrification enabled by V2X technologies (DriVe2X)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Climate, Energy and Mobility
Theme: Mobility
Destination: Clean and competitive solutions for all transport modes
Action type: Research and Innovation actions
Partnership: 2ZERO (co-programmed)
Countries involved: Finland,United Kingdom,Italy,Hungary,Netherlands,Portugal,Germany,Spain
Call: System approach to achieve optimised Smart EV Charging and V2G flexibility in mass-deployment conditions (2ZERO) (HORIZON-CL5-2021-D5-01, 2021)
Start date: 2023-01-01
End date: 2026-12-31
Duration (months): 48.0
Total project cost (EUR): 9204546.15
EU funding (EUR): 9204541.0
Participants: 23
Project status: SIGNED

This multidisciplinary four year project aims to establish a solid scientific base and stakeholder awareness for mass deployment V2X solutions. DriVe2X will develop new knowledge, tools, models, and technologies to cope with a V2X-based mass EV deployment in future. It will study and consolidate the understanding on the behavioural uncertainties linked to V2X and develop policy tools to support increasingly complex decisions on V2X roll-out in European smart cities. DriVe2X will implement advanced artificial intelligence techniques that efficiently capture the flexible energy potential from smart charging in building parking lots, homes, and charging stations, and match it with the distribution networks’s localized needs in order to research dynamic marketplaces for exchanging and trading V2X flexibility locally. DriVe2X will develop next-generation slow, lower-cost bidirectional charger units (from TRL3 to TRL7), that will be tested under different use cases in five demonstrators. DriVe2X embrace the EV user’s perceptions and expectations as critical success factors in V2X uptake and upscaling to a mass deployment future. Thus, DriVe2X innovates by inquiring and eliciting the social determinants of V2X, explicitly including it in the development of novel V2X technologies, tools and solutions. DriVe2X’s overall objective is to contribute to accelerate the uptake of V2X by i) deepening the state-of-the-art knowledge on this nascent field, ii) developing new V2X technologies and solutions suitable to mass EV deployment and iii) producing policy tools and insights in support of relevant decision makers. DriVe2X will advance state-of-the-art in V2X flexibility markets by establishing novel retailed marketplace, V2X charger technology by making them smarter, more efficient, cheaper and compact, the social side of V2X by providing empirical patterns and V2X upside studies by developing mass-deployment scenarios and roll-out strategies.

Keywords: mass deployment uptake scenarios,system level thinking,roll out strategy,v2x flexibility market,novel bidirectional v2x charger,social determinants of v2x,user perception,demonstrator
Inequalities in decision-making at critical junctions in life: The role of ability signals for sorting and selection (OPPORTUNITY)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Individuals, Markets and Organisations
Panel description: Economics, finance , management
Countries involved: Germany
Call: ERC STARTING GRANTS (ERC-2023-STG, 2023)
Start date: 2024-04-01
End date: 2029-03-31
Duration (months): 60.0
Total project cost (EUR): 1500000.0
EU funding (EUR): 1500000.0
Participants: 2
Project status: SIGNED

School track decisions, university or major choice, and initial job finding are all decisive for seizing life opportunities. When individuals from different socio-economic groups make different high-stakes decisions at these critical junctions, this can reinforce existing inequalities and lead to lock-in effects that are difficult to undo later in life. Understanding decision-making at these stages is thus fundamental to analyse and tackle inequality of opportunity. Individuals and evaluators make critical educational or occupational decisions under imperfect information due to uncertainties about true ability and future productivity. To resolve uncertainty, ability signals and their interpretation – both received and sent – are crucial components of decision-making, sorting and selection. OPPORTUNITY analyses the role of ability signals (such as grades, standardized tests, certificates, degrees, or CV information) for inequalities in high-stakes decision-making under uncertainty and inequality-reinforcing statistical and stereotypical discrimination by socio-economic background. To this end, it will draw on i) experimental methods in lab and field to study the role of social environment and ability signals for unequal educational and occupational choices; ii) administrative records to obtain information about absolute and relative ability signals (grades and degrees) awarded at various institutions over time; iii) econometric techniques that help exploit exogenous variation to make causal inference about the role of SES and different ability signals for efficient sorting and selection. OPPORTUNITY will promote an innovative research agenda providing novel answers on how to mitigate the unfair and inefficient allocation of talent. It will inform about policies that provide resources or changing ability signals to enhance equality of opportunity, one of the most urgent topics in Europe’s diverse, aging, and increasingly segregated societies.

Keywords: equality of opportunity,educational choice,uncertainty,socio economic inequalities
Organ- and cell type-specific responses to temperature in plants (OCELL)

Framework: Horizon Europe — Excellent Science
Programme: Marie Skłodowska-Curie Actions (MSCA)
Theme: Marie Skłodowska-Curie
Destination: Postdoctoral Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Life Sciences
Countries involved: Spain
Call: MSCA Postdoctoral Fellowships 2024 (HORIZON-MSCA-2024-PF-01, 2024)
Start date: 2026-01-16
End date: 2028-01-15
Duration (months): 24.0
Total project cost (EUR): 194074.56
EU funding (EUR): 194074.56
Participants: 1
Project status: SIGNED

Plants demonstrate remarkable adaptability to their environments, with temperature being a key factor in shaping their development. Understanding how plants perceive and respond to temperature fluctuations, particularly within non-stressful ranges (12–28ºC for Arabidopsis thaliana), is critical for optimizing plant fitness in the context of climate change. This project aims to unravel the mechanisms regulating temperature signaling in plants, focusing on how different organs, tissues, and cell types perceive and react to temperature changes. To address these challenges, we will first define and validate organ-specific gene regulatory networks (GRNs) in roots and shoots (WP1) and investigate how temperature variations in the aerial parts influence root behavior using a novel experimental system (WP2). Additionally, we will explore cell-type-specific GRNs through single-cell RNA sequencing of leaves under different temperature conditions (WP3). This interdisciplinary approach, supported by leading research institutions, is expected to provide new insights into plant temperature signaling. The findings will enhance our long-term capacity to predict and manipulate plant responses to climate change, with potential applications in agriculture and environmental sustainability. Furthermore, this project will significantly advance my expertise in plant molecular biology and bioinformatics, positioning me as a leader in plant-environment interactions .

Keywords: thermomorphogenesis,climate change,gene regulatory networks,transcription factors,arabidopsis
Learning analytics and Generative AI to design and facilitate learning (LEARNGENAI)

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: Norway
Call: MSCA Postdoctoral Fellowships 2024 (HORIZON-MSCA-2024-PF-01, 2024)
Start date: 2026-02-02
End date: 2028-02-01
Duration (months): 24.0
Total project cost (EUR): 251578.56
EU funding (EUR): 251578.56
Participants: 2
Project status: SIGNED

Recent advancements in AI, specifically Generative AI (GenAI), have started to significantly impact how teaching and learning occur and how the respective institutions operate. GenAI will have to function in learning spaces and contexts alongside other activities. Therefore, it is expected that instruction and GenAI will complement each other and stand by each other. Yet, there is a limited understanding of how teachers/learners and AI interact, understand, respect, and learn from each other. The purpose of LEARNGENAI is to examine how GenAI is used and under which circumstances it has the potential to improve teaching and learning. The aim is to identify the critical success factors in a range of contexts, and use these findings to promote research and practice. Following an interdisciplinary approach that bridges the domains of computer science, pedagogy and educational sciences, teacher education, learning analytics, and learning theory, along with recent advances in technological tools and AI-based methods, the present project will explore how GenAI is effectively leveraged in education and attempt to isolate key success factors. Therefore, the main focus of the project will be to identify various strategies for implementing GenAI in learning and the success factors to support hybrid learning, understand how information sources, technological infrastructure and human skills and knowledge are ethically coalesced to achieve desired ends, and develop GenAI prototype applications and assess their impact in learning. By empirically investigating these issues and developing a framework of best practices, the ultimate goal is to guide educational institutions in their transition into the artificial intelligence era and enable them to enhance student experiences through personalized learning opportunities.

Keywords: learning technology,learning analytics,ai in education,learning design
BlueGreen Governance (BlueGreen Governance)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Food, Bioeconomy Natural Resources, Agriculture and Environment
Theme: Bio-Environment
Destination: Innovative governance, environmental observations and digital solutions in support of the Green Deal
Action type: Research and Innovation actions
Countries involved: Belgium,Italy,Norway,Spain,Estonia,Slovenia,France,United Kingdom,Netherlands
Call: Multi-layer governance performance of marine policies (HORIZON-CL6-2022-GOVERNANCE-01, 2022)
Start date: 2024-01-01
End date: 2027-12-31
Duration (months): 48.0
Total project cost (EUR): 4981157.55
EU funding (EUR): 4981157.55
Participants: 17
Project status: SIGNED

The main goal of BlueGreen Governance is to develop innovative land-sea governance schemes based on scientific evidence and societal choices. The current management of oceans, seas and coasts is fragmented across multiple institutional layers and policy areas and based on past experience. BlueGreen Governance pursues an innovative approach to the governance of the seas and coastal areas that: promotes integration between institutional layers and across policy sectors with a clear impact on the use of the land and the sea; involves and engages citizens in decision-making processes, while at the same time including scientific evidence; responds predictively to changing physical conditions as indicated by scientific evidence as well as indigenous and local knowledge and citizen science; and uses e-governance tools in support of the previous three points. With this focus and approach, the project responds to the need for better-informed decision-making processes, social engagement and digital innovation while promoting more harmonious and effective science-policy-society interfaces. The promotion of better science-policy, science-society and society-policy interactions will be embedded in the digital transformation and application of e-governance tools for co-design and service delivery. BlueGreen Governance will implement and assess these innovative governance schemes in 8 cases across several European regions and sea basins and will draw lessons on how to trigger and facilitate effective institutional change via capacity building. The cases are: Comunidad Valenciana; North Adriatic; the Solent; Western Scheldt; Oslofjord; Canary Islands and Reunion. With this geographical scope, the project will investigate five marine basins (Western Mediterranean Sea, Eastern Mediterranean Sea, North Sea, Atlantic Ocean and Indian Ocean), including one transnational marine basin (i.e. the North Adriatic case) and one transnational river basin (i.e. the Western Scheldt case).

Keywords: biodiversity water climate nexus,marine policies,land sea management,science advice,science policy interface,stakeholders engagement,co creation,deliberation,digital tools,e governance
ATTRIBUTING AND VERIFYING EUROPEAN AND NATIONAL GREENHOUSE GAS AND AEROSOL EMISSIONS AND RECONCILIATION WITH STATISTICAL BOTTOM UP ESTIMATES (AVENGERS)

Framework: Horizon Europe — Global Challenges and European Industrial Competitiveness
Programme: Climate, Energy and Mobility
Theme: Climate
Destination: Climate sciences and responses for the transformation towards climate neutrality
Action type: Research and Innovation actions
Countries involved: Sweden,Germany,Italy,Netherlands,Finland,Switzerland,Cyprus
Call: Verification and reconciliation of estimates of climate forcers (HORIZON-CL5-2022-D1-02, 2022)
Start date: 2023-01-01
End date: 2026-12-31
Duration (months): 48.0
Total project cost (EUR): 5499941.75
EU funding (EUR): 5499941.0
Participants: 15
Project status: SIGNED

The Attributing and Verifying European and National Greenhouse gas and aerosol Emissions and Reconciliation with Statistical bottom-up estimates – AVENGERS consortium unites a diverse group of experts “to establish how top-down techniques can support the verification of national greenhouse gas (GHG) inventories and other regulated estimates of emissions and removals, in order to improve or supplement the methods/approaches currently used”. In recognition of the need for a truly multi-actor approach the team represents experts in atmospheric inverse modelling and data assimilation, remote sensing, environmental monitoring and observation, terrestrial ecosystem modelling, policy and stakeholder interaction together with national inventory compilers. AVENGERS builds upon the heritage of previous EU projects, in particular CHE, CoCO2 and VERIFY and will advance the top-down approach for quantifying GHG and aerosol emissions, as well as reconcile and integrate approaches into a joint bottom-up and top-down framework. Based on the reconciliation AVENGERS will prepare good-practice guidelines for use of atmospheric inverse models as well as develop a Flexible Inversion Tool for Inventory Compilers (FIT-IC). Knowledge and outcomes are provided of use for policy and societal stakeholders and replicable outside of our chosen target area (Europe with a focus on Germany, Italy, Sweden, Switzerland, and The Netherlands). The uniqueness of the consortium lies in the combination of the required scientific expertise with full partner presence of official reporting agencies from exemplary EU countries (Germany, Italy, Sweden, and The Netherlands) facilitating the transfer of knowledge to key international organizations (such as UNFCCC, WMO) in the field.

Keywords: greenhouse gas emissions source attribution reconciliation of emissions national inventories ecosystem and atmospheric modelling tracer transport inversions data assimilation remote sensing
Cognitive Sparse MIMO Automotive Radar with Embedded Communications: Enabling Safer Driving in Greener Environment (CS-MIMOAR-EC)

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: Germany
Call: MSCA Postdoctoral Fellowships 2022 (HORIZON-MSCA-2022-PF-01, 2022)
Start date: 2024-01-01
End date: 2025-12-31
Duration (months): 24.0
Total project cost (EUR): 189687.36
EU funding (EUR): 189687.36
Participants: 1
Project status: CLOSED

Automotive radar provides a 360-degree surround sensing capability under all weather conditions and makes driving safer, with or without a hand on the steering wheel, thus presenting itself as viable and affordable solution to EU's ambitious "Vision Zero" goal in road safety. Future automotive radar devices need to adhere to cost and size restrictions while still addressing the required performance requirements, such as high resolution 5-Dimensional point cloud sensing, increased susceptibility to mutual interferences and embedded V2X communication in automotive sensing in a low-power, low-cost and compact design. In response to that, this fellowship aims to investigate cognitive sparse MIMO automotive radar sensing with embedded communication, which can counteract the dilemmas between performance and overhead, in turn enabling a safer driving in a greener environment. So far, no existing automotive radar has attempted to add cognizance of MIMO array configuration and transmit waveforms for performance improvement. The proposed work goes against the common regime of conventional automotive radar, where both a fixed MIMO array and transmit waveforms are assumed regardless of a dynamic road scene, and embarks upon a novel sparse automotive sensing paradigm with cognition, where array reconfigurability and waveform optimization are incorporated into the "perception-action" cycle by antenna selection via radio frequency switches. Successful achievements of this fellowship will lead to a timely and useful contribution to automotive radar and relevant communities, including: (1) novel techniques of joint automotive sensing and full duplex communication, (2) systematical simulations and experimental data, (3) an initial prototype implemented on a digital, software defined radar platform. In the long term, the successful achievement of this project will reduce deaths and injuries on European roads once upon commercialization.

Keywords: sparse signal processing,mimo arrays,radar signal processing
The 'Vanishing Third Party': Access to Justice, Procedural Justice and Substantive Justice in the Age of Dispute Resolution Automation (DRA)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Institutions, Governance and Legal Systems
Panel description: Political science, international relations, law
Countries involved: Israel
Call: ERC CONSOLIDATOR GRANTS (ERC-2021-COG, 2021)
Start date: 2023-03-01
End date: 2028-02-29
Duration (months): 60.0
Total project cost (EUR): 1971671.0
EU funding (EUR): 1971671.0
Participants: 1
Project status: SIGNED

Courts without judges? Informal processes without mediators? We are on the verge of a dramatic paradigm shift in our justice system from human to algorithmic dispute resolution. While most of us perceive the delivery of justice as a human task, courts and informal arenas worldwide are increasingly relying on online processes that employ algorithms in lieu of judges and mediators. Also, artificial intelligence is more commonly used to support and predict judicial outcomes. Together, these developments constitute dispute resolution automation (“DRA”). What is still a nascent phenomenon, accelerated by Covid-19, is certain to expand dramatically within and outside courts, as our backlogged justice system seeks to transform the inefficient resolution and adjudication of cases, and expand access to justice. Despite growth of DRA, our theories, concepts, and rules remain focused on the role of human third parties in reducing barriers to courts, generating perceptions of procedural fairness, and ensuring just outcomes. The implications of the “vanishing third party” on justice have yet to be recognized, studied, conceptualized, and theorized. This project will fill this gap by offering a new theory of access to justice (barriers), procedural justice (perceived fairness) and substantive justice (just outcomes) for DRA. To that end, I will (1) develop a normative framework for just DRA, (2) conduct a comprehensive empirical study of real data in four research sites in different countries (France, the Netherlands, the U.S. and Israel), lab experiments on DRA, and interviews with designers and users of DRA; (3) generate a typology for the design of just automated processes across settings, and devise model procedural rules for the adoption of DRA in courts, and (4) develop novel methods for the evaluation of DRA.

Keywords: courts,judges,law and technology,alternative dispute resolution,online dispute resolution,access to justice,procedural justice,artificial intelligence,ai,negotiation,mediation,justice