Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Collaborative Development Framework for Electric Software-defined Vehicles (CODE4EV)

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: Germany,Poland,Austria,Slovenia,Italy,Belgium,Spain,France,Romania
Call: Advanced digital development tools to accelerate the development of software defined vehicles that enable zero-emission mobility (2ZERO Partnership) (HORIZON-CL5-2024-D5-01, 2024)
Start date: 2025-01-01
End date: 2027-12-31
Duration (months): 36.0
Total project cost (EUR): 4995981.26
EU funding (EUR): 4995981.25
Participants: 15
Project status: SIGNED

The CODE4EV project aims to accelerate the development of electric software-defined vehicles (SDVs) by establishing a collaborative development framework. This framework will support the design, production and operational phases of electric vehicles (EVs) by demonstrating its application through selected Use Cases relevant to emerging and future SDV architectures. The project key objectives include the elaboration of digital design tools and a trustworthy development methodology for electric SDVs, improving the efficiency and reliability of SDV architecture component sharing, and accelerating validation processes. The project also focuses on the implementation of a model-based design, the development of a symbolic ontology knowledge database, and the migration from rapid prototyping environments to automotive SW environments to improve development processes and compliance with industry standards. In addition, CODE4EV aims to provide multi-layered benefits throughout the design, production and operational phases of EVs. This includes methods for defining the SDV architecture, real-time runtime virtualisation approaches, and developing modular HW architectures to optimise data usage. The project Use Cases will demonstrate the implementation of the collaborative development framework, such as data-driven EV optimisation, health monitoring and predictive maintenance, and smart motion control. These Use Cases aim to demonstrate improvements in energy consumption, component life extension and overall vehicle performance. CODE4EV plans to develop virtual, hybrid and full-scale demonstrators of electric SDVs for different vehicle categories, focusing on efficient verification procedures and the evaluation of the scalability of the CODE4EV approach. These efforts aim to ensure compatibility and efficiency for a range of vehicle types, including heavy-duty trucks and L-class EVs, thereby making an important contribution to the promotion of zero-emission mobility solutions.

Keywords: electric vehicles,software defined vehicles,collaborative design,validation and verification,simulation and modelling
Discovering the World Through Unsupervised Statistical Relational Learning (DISCWORLD)

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: Belgium,United States
Call: MSCA Postdoctoral Fellowships 2023 (HORIZON-MSCA-2023-PF-01, 2023)
Start date: 2024-09-01
End date: 2026-08-31
Duration (months): 24.0
Total project cost (EUR): 177322.08
EU funding (EUR): 177322.08
Participants: 2
Project status: SIGNED

Machine learning is popular nowadays, thanks to the impressive results achieved by systems like DeepMind’s AlphaGo, OpenAI’s language prediction model GPT-3 or Amazon’s speech recognition system Alexa. At the basis of these successes, there is representation learning, which enables training deep neural networks in an unsupervised fashion and provides the starting conditions for subsequent task-specific training. However, current representation learning strategies use large neural networks and consume large amount of data, thus being data and energy inefficient. In contrast, humans learn from limited data in a very efficient way. This is due to the fact that humans are able to perform reasoning, while representation learning strategies lack such capability. This research project aims to overcome these limitations by providing the mathematical foundations for the integration between unsupervised learning and reasoning AI systems based on logic. Specifically, the aim is to devise algorithms enabling the discovery of symbolic representations from noisy/ambiguous data together with their relations and being able to adapt the acquired relational knowledge over time. The resulting solutions will be applied to improve image understanding in autonomous driving and to gain insights about causal reasoning and learning symbolic abstractions in mathematical domains.

Keywords: probability,logic,program synthesis,unsupervised learning,deep learning,continual learning
Seismic noise interferometry for stress analysis (SIRENS)

Framework: Horizon Europe — Excellent Science
Programme: Marie Skłodowska-Curie Actions (MSCA)
Theme: Marie Skłodowska-Curie
Destination: Postdoctoral Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Environment and Geosciences
Countries involved: Spain
Call: MSCA Postdoctoral Fellowships 2024 (HORIZON-MSCA-2024-PF-01, 2024)
Start date: 2025-04-01
End date: 2027-03-31
Duration (months): 24.0
Total project cost (EUR): 194074.56
EU funding (EUR): 194074.56
Participants: 1
Project status: SIGNED

As global warming accelerates, the need for effective solutions to reduce greenhouse gas emissions has become critical. Many of these solutions—such as geothermal energy, carbon capture and storage, and critical minerals for clean energy technologies—are found in the subsurface. The safe and sustainable use of these subsurface resources requires a precise understanding of the Earth’s stress field. Traditional methods for determining stress directions, particularly at reservoir and local scales, rely on costly and invasive borehole measurements. The SIRENS project offers a non-intrusive, cost-effective alternative by using passive seismic sensing to estimate the maximum horizontal stress direction (SHmax) at various spatial scales. This method capitalizes on the behaviour of rocks under compression, where crack closure is influenced by local stress conditions, with the fastest closure occurring in the direction of SHmax. By leveraging the ever-present seismic noise and Earth’s solid tides, it is possible to determine the SHmax direction. SIRENS will refine this technique, focusing on its application in the Iberian Peninsula. With its diverse tectonic landscape, extensive subsurface resources, and wealth of seismic data, Iberia serves as an ideal testing ground for advancing the methodology. The project will generate new insights into stress field estimation across different scales and provide a SHmax direction dataset that will contribute to Iberia’s energy transition. SIRENS is committed to open science by making its tools and datasets freely accessible to the public. Collaborating with academic institutions and industry will ensure that the project’s outcomes are tailored to the needs of key stakeholders. Moreover, SIRENS will pave the way for future innovations in diverse geological settings, ensuring the project’s long-term impact. Ultimately, the project aligns with the Green Charter by working with a low-carbon, sustainable and cost-effective approach.

Keywords: seismology,seismic noise interferometry,stress field,earth s solid tides
Band-resolved imaging and nonlinear optical control of currents in topological materials (PhotoTopoCurrent)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Condensed Matter Physics
Panel description: Structure, electronic properties, fluids, nanosciences, biological physics
Countries involved: Israel
Call: ERC STARTING GRANTS (ERC-2022-STG, 2022)
Start date: 2023-10-01
End date: 2028-09-30
Duration (months): 60.0
Total project cost (EUR): 2316250.0
EU funding (EUR): 2316250.0
Participants: 1
Project status: SIGNED

Topological materials have captured the imagination of scientists with unique electronic dispersions and surface states. While their potential seems huge - from advanced photodetectors to spintronic devices - so far it has not come to fruition, despite two decades of research. In this proposal, my aim is to reveal and control light-matter interactions, electron populations, and currents in topological bands by combining two fields of research: topological materials and nonlinear optical coherent control. Nonlinear quantum coherent control was a major leap in ultrafast science, enabling optical control of chemical reactions and electronic processes in atoms and molecules on femtosecond time scales. In solid-state systems, despite some pioneering experiments, coherent control has not been widely used. This is partially due to the complex band structures and partially because transport research has tended to be more easily applicable to the solid-state realm. Topological materials, however, are especially promising candidates for coherent control, because (a) it has proven hard to access properties related to the topology in 3D materials via transport, and (b) topological bands are associated with unique optical selection rules, and as recently revealed – fascinating nonlinear optical phenomena. In this project I will develop nonlinear coherent control of photocurrents in topological materials, thus building a bridge between nonlinear control to transport measurements of topological bands. I will use time-resolved ARPES – a powerful tool providing band-imaging out of equilibrium – to enable imaging of the photocurrents within the topological bands. PhotoTopoCurrent will establish a new research direction, which will provide a deep understanding of the unique optical couplings and nonlinear optical responses of topological electronic bands, allow us to develop sophisticated optical schemes for tailored control, and finally implement them in transport devices.

Keywords: topological materials,photocurrent,ultrafast,trarpes
Immunopeptidomics-based Development of Next-Generation Bacterial mRNA Vaccines (BAXERNA 2.0)

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: Belgium,Sweden,France,Netherlands,Switzerland,Malta
Call: Vaccines 2.0 - developing the next generation of vaccines (HORIZON-HLTH-2022-DISEASE-06-two-stage, 2022)
Start date: 2023-07-01
End date: 2028-06-30
Duration (months): 60.0
Total project cost (EUR): 9458801.25
EU funding (EUR): 9458801.25
Participants: 11
Project status: SIGNED

BAXERNA 2.0 will establish a new vaccine development pipeline based on dramatically improved immunopeptidomics screening and innovative mRNA vaccine formulation. We will use our powerful new pipeline to develop novel mRNA vaccines against three bacterial pathogens that can persist within phagocytic cells: Mycobacterium tuberculosis (MTB), Mycobacterium ulcerans (MU), and Acinetobacter baumannii (AB). MTB and AB are clinically problematic bacteria with alarming levels of antimicrobial resistance (AMR), while MU is an important neglected tropical disease. Although vaccines are recognized as highly effective tools to mitigate AMR and tropical diseases, effective vaccine development for these (facultative) intracellular bacteria is held back by a lack of known antigens, and by current vaccine platforms struggling to elicit the required strong cellular immune responses. We will overcome both limitations here through two key innovations: (i) novel proteomics and proteomics informatics approaches for immunopeptidomics to allow highly sensitive discovery and prioritization of bacterial epitopes presented on infected cells; and (ii) novel mRNA vaccines to induce both humoral and cellular immune responses, with innovative adjuvants to strengthen adaptive immunity, and to modulate innate immunity. Vaccine production will be done according to GMP standards, and we will pursue novel, low-cost production methods to enable local production and much-needed improved vaccine stability. We will characterize innate and adaptive immune responses in detail in human cellular models and mouse infection models. In addition, top vaccine candidates for MTB will be evaluated in unique primate models, followed by testing of the lead candidate in a first-in-human Phase I clinical trial. Together, we will establish our novel vaccine development pipeline as a blueprint for world-leading, next-generation bacterial vaccine development.

Keywords: tuberculosis,buruli ulcer,acinetobacter baumannii,mass spectrometry,proteomics,bioinformatics,mrna vaccine,adjuvants,innate immunity,trained immunity,lipid nanoparticle,clinical trial
Inferring the cross-platform structures of socio-political POLARization around CLIMATE change on social media (POLARCLIMATE)

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: Social Sciences and Humanities
Countries involved: Austria,United Kingdom
Call: MSCA Postdoctoral Fellowships 2023 (HORIZON-MSCA-2023-PF-01, 2023)
Start date: 2024-10-01
End date: 2026-09-30
Duration (months): 24.0
Total project cost (EUR): 183600.96
EU funding (EUR): 183600.96
Participants: 2
Project status: SIGNED

Addressing climate change's existential threat demands a global effort to rapidly reduce carbon emissions. However, political gridlock fuelled by societal polarisation can stymie effective action. POLARCLIMATE confronts this challenge, studying interventions to curb climate scepticism and foster consensus. POLARCLIMATE will use a social media lens to advance the current state-of-the-art on climate polarisation, tackling three key research challenges. First, existing studies of climate polarisation fail to consider how passive engagement with climate narratives shape climate opinions. Second, studies of climate polarisation are typically siloed to a single platform, limiting their demographic relevance and preventing a robust cross-platform analysis of content and structure. Finally, studies do not adequately consider the agents who drive climate polarisation, especially those organisations with a vested financial interest in maintaining the status quo. To address these challenges, POLARCLIMATE will take an interdisciplinary, multi-modal approach drawing on quantitative methods from networks and data science, and qualitative methods from climate communications. The analysis of passive consumption and cross-platform structure will be studied using Bayesian inference and network reconstruction methods, and content will be analysed using taxonomies of climate scepticism and computer-aided topic modelling across various climate publics. Finally, organisational financial interests will be identified by using Bloomberg financial data, and compared to the rhetoric of corresponding organisations on social media. POLARCLIMATE will balance scientific rigour with policy relevance. The project's outputs will offer multi-modal evidence to inform the development of concrete policy recommendations aimed at mitigating the adverse impacts of climate polarization. These recommendations aim to contribute to a more unified and effective global response to climate change.

Keywords: network science,social media,climate change,political polarisation,social networks,misinformation,disinformation,statistical inference,climate denial,climate scepticism,multilayer networks
Activated Mechanophores for the Development of Mechanically Adaptative Materials (ActiMech)

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: Chemistry
Countries involved: Spain
Call: MSCA Postdoctoral Fellowships 2024 (HORIZON-MSCA-2024-PF-01, 2024)
Start date: 2025-07-01
End date: 2027-06-30
Duration (months): 24.0
Total project cost (EUR): 194074.56
EU funding (EUR): 194074.56
Participants: 1
Project status: SIGNED

ActiMech aims to establish a groundbreaking class of stimuli-responsive molecules that merge the unique properties of activated carboxylic acids (ACAs) and triarylmethane-based mechanophores. These innovative Activated Mechanophores (AMs) will be developed to serve as mechanochromophores and initiators of reactions in solid-state materials, paving the way for novel mechanically adaptive materials. The systems incorporating AMs will be able to change both their color (even multiple times) and rheological properties in response to mechanical forces. The successful execution of this project will allow, in the long run, the fabrication of advanced materials that can serve as damage sensors in industrial facilities and instrumentation, enhancing the safety of places and technical operations. ActiMech is structured around three specific objectives that involve (1) the synthesis and characterization of AMs and their functionalization with linear polymer chains, (2) the investigation of the operating mechanisms of AMs under mechanical stimulation, and (3) the analysis and exploitation of AMs behavior in a solid polymeric matrix to promote solid-state reactions. Through these objectives, ActiMech will combine the dissipative properties of ACAs and those of triarylmethane-based mechanophores, bringing together two classes of compounds of increasing popularity in chemical science. Overall, ActiMech will allow me to develop the skills and expertise necessary to establish myself as an independent researcher and a future organic chemistry professor in Europe. This fellowship will provide me with the unique opportunity to conduct cutting-edge research in a leading technical institution, such as the Institut Català d'Investigació Química (ICIQ), and under the careful supervision of Dr. José Augusto Berrocal, a young and rising leader in the field of polymer science.

Keywords: mechanophores,activated carboxylic acids,polymers,dissipative systems,mechanochromophores,adaptative materials,smart materials
Debiasing the uncertainties of climate stabilization ensembles (EUNICE)

Framework: Horizon Europe — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Human Mobility, Environment, and Space
Panel description: Human geography, demography, health, susta inability science, territorial planning, spatial analysis
Countries involved: Italy
Call: ERC CONSOLIDATOR GRANTS (ERC-2021-COG, 2021)
Start date: 2022-12-01
End date: 2027-11-30
Duration (months): 60.0
Total project cost (EUR): 1995000.0
EU funding (EUR): 1995000.0
Participants: 2
Project status: SIGNED

Mathematical models have become central tools in global environmental assessments. To serve society well, climate change stabilization assessments need to capture the uncertainties of the deep future, be statistically sound and track near-term disruptions. Up to now, conceptual, computational and data constraints have limited the quantification of uncertainties of climate stabilization pathways to a narrow set, focused on the current century. The statistical interpretation of scenarios generated by multi-model ensembles is problematic due to availability biases and model dependencies. Scenario plausibility assessments are scant. Simplified, single-objective decision criteria frameworks are used to translate decarbonization uncertainties into decision rules whose understanding is not validated. EUNICE aims to transform the methodological and experimental foundations of model-based climate assessments through quantification and debiasing of uncertainties in climate stabilization pathways. Our approach is threefold: construct, consolidate and convert. We first apply simulation and statistical methods for extending scenarios into the deep future (beyond the current century and status quo), quantifying and attributing deep uncertainties. We consolidate model ensembles through machine learning and human ingenuity to eliminate statistical biases, pin down near-term correlates of long-term targets, and identify early signals of scenario plausibility through prediction polls. Finally, we use decision-theoretic methods to convert model-generated maps of the future into resilient recommendations and experimentally test how to communicate them effectively. By advancing the state of the art in mathematical modelling, statistics, and behavioural decision-making, we strengthen the scientific basis of climate assessments, such as those of the IPCC. The approach and insights of EUNICE can be applied to other high-stakes environmental, social and technological evaluations.

Keywords: climate change mitigation climate emission pathways model ensembles uncertainty quantification uncertainty communication behavioural science
EUROPEAN LABOUR MARKETS UNDER PRESSURE – NEW KNOWLEDGE ON PATHWAYS TO INCLUDE PERSONS IN VULNERABLE SITUATIONS (PATHS2INCLUDE)

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: Norway,Luxembourg,Romania,Italy,Spain,Germany,Poland,Belgium
Call: Overcoming discrimination for an inclusive labour market (HORIZON-CL2-2022-TRANSFORMATIONS-01, 2022)
Start date: 2023-03-01
End date: 2026-02-28
Duration (months): 36.0
Total project cost (EUR): 2871030.0
EU funding (EUR): 2871030.0
Participants: 8
Project status: SIGNED

PATHS2INCLUDE will provide new, gender-sensitive, comparative knowledge-base on effective employment policies targeted at developing inclusive labour markets for persons in vulnerable situations in Europe. The study will examine the importance of intersectionality related to how context creates vulnerability, by focusing on three central labour-market processes: recruitment; career trajectories; and work exit. Through the involvement of national and European stakeholders, PATHS2INCLUDE aims to develop proposals for effective policies and to inform relevant policymakers with a view to maximising the project’s impact from a societal as well as scientific perspective. The project will combine diverse methods, data and disciplines (economics, political science and sociology) in innovative ways: (1) harmonised factorial survey experiment combined with qualitative interview studies with employers in four European countries (DE, NO, PL and RO); (2) causal analyses of comparative microdata; (3) microsimulation analysis exploiting the EUROMOD infrastructure. Linking the analyses of these data and the three central labour-market processes, will give original insights on how institutional and contextual factors shape barriers or mitigate risk of labour-market attachment among persons in vulnerable situations. These insights could include cross-national differences in employment-protection legislations and facilitation of care, regional differences in demand for labour, differences at company level related to the size of the firm, flexibilities in job tasks, and conditions that were affected by the COVID-19 pandemic, covering unemployment rates and infection-control measures across different segments of the labour market. The project will be implemented by an interdisciplinary consortium of seven research institutions and one European civil society organisation. The consortium has a balanced composition in terms of gender, stage of the career and area of expertise.

Keywords: inclusion,discrimination,european labour markets,inclusion policies,vulnerabilities