Data-Gouv-ML/pcri-actions-marie-curie-participations-et-projets-laureats huggingface dataset:
Projet AI-BRIDGES (Horizon Europe, appel à projets 2024)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Social Sciences and Humanities
Statut du projet : SIGNED

AI-BRIDGES: Enhancing Cultural Heritage Impact through Generative AI and Linked Open Data  AI-BRIDGES addresses the disconnect between Cultural Heritage Institutions (CHIs), Linked Open Data (LOD) platforms like Wikidata, and Generative AI (GenAI), limiting their potential to create a robust, interconnected, responsible and impactful data ecosystem. The project builds three key bridges to overcome this challenge: 1. CHIs and LOD: Developing a low-code/no-code pipeline to streamline CHIs' integration and management of cultural heritage metadata in LOD platforms, reducing technical complexity and improving data accessibility at scale. 2. LOD and LLMs: Utilizing Retrieval-Augmented Generation (RAG) to connect LOD platforms with large language models (LLMs), resulting in more accurate, inclusive, and culturally relevant AI outputs aligned with Responsible and Explainable AI practices. 3. Users and LOD: Engaging students in participatory data curation to address bias, misinformation, and knowledge gaps in cultural heritage narratives while enhancing digital literacy.  AI-BRIDGES will combine interdisciplinary approaches from Digital Humanities (DH), AI, education, and cultural heritage management to create scalable, open-access tools and methodologies that benefit CHIs, researchers, and the public, while advancing the responsible integration of GenAI into the cultural heritage sector.  The project is also designed as a career development initiative to help the researcher build interdisciplinary expertise in DH, AI, and cultural heritage data management, facilitating opportunities for significant contributions to the integration of GenAI with LOD platforms. As an established leader in Open Knowledge and digital education, the fellowship will enable her to make significant contributions to the integration of GenAI with LOD platforms, advancing her career toward founding an innovation center that bridges CHIs, education, and transformative technologies like GenAI.

Mots clés : generative ai genai , cultural heritage, linked open data lod , responsible ai, data literacy, data curation, data extraction, rag

Participants :
- Université de Londres (Royaume-Uni) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 276187.92 €
Projet 2DMagspin (Horizon Europe, appel à projets 2023)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Physics
Statut du projet : SIGNED

Although graphene is known for its superlative electronic properties, it is intrinsically characterized by weak spin-orbit coupling and paramagnetic nature at low temperature. We propose to circumvent this issue by combining graphene with transition metal magnetic oxides (graphene/MO) in a unique series of artificially layered heterostructures. The presence of the magnetic oxide layer will allow us to investigate proximity induced magnetism and anomalous Hall effect (AHE) in graphene due to the presence of high order d-orbitals and ensuing charge transfer induced hybridization. To that aim, perovskite manganate oxides will be initially investigated and then extended to cobalt or magnesium-based ferrites for room temperature applicability. Finally, we aim at fabricating novel graphene/MO-based heterostructure spin devices to test their feasibility for spin transport applications.

Mots clés : 2d materials, magnetic oxides, heterostructures, spintronics

Participants :
- Université technique de Vienne (Autriche) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 183600.96 €
Projet REMODEL (Horizon Europe, appel à projets 2023)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Mathematics
Statut du projet : CLOSED

Reasoning with and about norms is central in a variety of fields, ranging from legal reasoning to machine ethics. Deontic logics provide a logical framework to give a formal and rigorous presentation of the inferential patterns involved in normative reasoning. However, beside describing and representing, it is crucial to have explanations as to why a certain norm applies or not. In logic, positive explanations come in the form of derivations, whereas negative explanations can be identified with countermodels.  The REMODEL (StRucturEs for MOdal and DEontic Logics) project will introduce proof calculi to analyze and explain normative reasoning. The calculi thus introduced will be Gentzen-style systems, i.e., analytic proof calculi in which the proofs result from a stepwise decomposition of the conclusion according to rules.  The researcher has carried out several studies on the proof theory of modal and related logics and he aims to use his expertise in the field to carry out a dedicated research program in deontic logics. The REMODEL project will tackle the problem of defining analytic proof systems for monotonic deontic logics and non-monotonic ones, employing the methodologies of structural proof theory. Finally, he will study the relation between deontic and modal logics establishing formal embeddings via proof transformations. The collaboration with the supervisor will be essential and the researcher will benefit from her experience both in the fields of proof theory and deontic logics.

Mots clés : proof theory deontic logics modal logics normative reasoning

Participants :
- Université technique de Vienne (Autriche) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 183600.96 €
Projet SCI-PHI (Horizon Europe, appel à projets 2022)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Chemistry
Statut du projet : SIGNED

In the light of environmental and energy supply crises, new technologies which can satisfy mankind's energy demand in a sustainable way must be developed. Heterogeneous photocatalysis is one of the most promising fields of research in this regard, as it allows to directly convert energy in sunlight to chemical bonds in molecules. The process is conceptually similar to the photosynthetic process in plants, but a semiconductor is used to absorb the light and catalyze the reactions. In reality, a so-called co-catalyst on the semiconductor surface is necessary for proper function, and this usually means depositing noble metal particles on the surface. Exactly how such nanoparticles work is usually elusive, and this makes the rational design and optimization of photocatalysts difficult. The high costs of the precious metal is also a major roadblock making the upscaling of a lab-scale photocatalyst economically unviable. If we can better understand the role of the co-catalyst, we can develop new strategies to reduce or even replace this material with more economical alternatives. In this proposal, we intend to apply the concept of single-atom catalysis to heterogeneous photocatalysis. Single noble metal atoms such as Platinum, Rhodium or Iridium will be deposited directly on semiconductor single crystal surfaces of Titanium dioxide and hematite (Iron oxide) with a precisely known structure. Highly sensitive surface science methods, including scanning probe microscopy as well as spectroscopic and spectrometric techniques, will be used to characterize the local structure of the active site, and relate it to the catalytic function. The project combines the expertise of the host group in the synthesis and characterization of single atoms on metal oxides, and the applicant’s practical knowledge of UHV photocatalysis, and will ultimately establish clear structure-performance relationships in order to facilitate the rational design of economically viable photocatalysts.

Mots clés : single atom catalysis, surface science, ultra high vacuum

Participants :
- Université technique de Vienne (Autriche) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 183600.96 €
Projet RePolHyCA (Horizon Europe, appel à projets 2024)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Chemistry
Statut du projet : SIGNED

As the world increasingly recognizes the urgency of sustainable resource management, the role of plastics has come under intense scrutiny. While commercial plastics offer essential benefits to modern society, their inherent drawbacks in end-of-life management have caused severe and long-lasting environmental damage. The REPOLHYCA project seeks to contribute to the crucial effort of developing next-generation plastics that combine high performance with full recyclability, aligning with the Plastics Strategy of the European Green Deal. The project will focus on aliphatic polyesters derived from bicyclic "hybrid" γ-butyrolactone monomers, which hold significant potential as useful and fully recyclable materials, capable of complete depolymerization back to their monomers, thus enabling a truly circular plastic economy. However, these materials’ performance and production costs are still unsatisfactory for commercial viability, mainly due to the complex and inefficient multistep processes required to access the monomers. The REPOLHYCA project aims to overcome these limitations by leveraging the expertise of Supervisor (Prof. Miquel Costas) in selective C-H functionalization and Fellow (Dr. Francesco Zaccaria) in catalytic polymerization. The project will develop an unprecedented library of structurally diverse hybrid lactone monomers using streamlined synthetic methodologies developed by the Supervisor’s group, enabling selective one-step γ-lactonization of abundant substrates (carboxylic acids) with earth-abundant metal catalysts (Mn) and environmentally friendly oxidants (hydrogen peroxide) under mild conditions. The Fellow’s expertise will then be applied to convert these new monomers into polyesters and assess their material properties and recyclability. The resulting structure-properties database will prove essential for identifying and designing advanced, sustainable polymers.

Mots clés : c h oxidation, recyclable polymers, depolymerization, polyesters, ring opening polymerization

Participants :
- Université de Gérone (Espagne) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 209914.56 €
Projet ChemCon (Horizon Europe, appel à projets 2023)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Information Science and Engineering
Statut du projet : SIGNED

The aviation industry is one of the largest consumers of fossil fuels and, therefore, a high emitter of CO2 and fine particles, which can contribute to the formation of contrails and can have adsorbed the carcinogenic Polycyclic Aromatic Hydrocarbons (PAHs). Emissions from this sector are expected to increase more and more due to its rapid growth. To address this problem, the use of alternative fuels from renewable biomass and waste resources, such as Sustainable Aviation Fuels (SAFs), has been receiving increasing attention. Sustainable aviation is the goal of EU due to its need to reduce transport emissions by 90% by 2050 (compared to 1990 levels). Suitability and validity of SAFs need to be evaluated prior to use in engines/turbines and it can be done with high-quality experimental data in conjunction with chemical kinetic models, which is a valuable tool to investigate reaction pathways and to guide fuel and engine design. However, there is a lack of experimental data obtained at laboratory-scale and chemical kinetic models describing the combustion of SAFs. Thus, this project aims to provide new experimental data and a kinetic model for the conversion of p-cymene, an aromatic terpenoid produced using several natural feedstocks, in order to contribute with the comprehensive understanding of its combustion behavior and its capacity to form fine particle and PAHs. The experiments will be conducted under a wide range of operational conditions of interest for combustion using the facilities and equipment available at the Aragon Institute of Engineering Research (I3A) of the University of Zaragoza. ChemCon will be conducted in the multi-disciplinary Thermochemical Processes Group (GPT), with the guidance of Prof. María U. Alzueta, expert in the field of minimizing pollutant emissions in combustion processes. This project will strengthen my skills and independence, expanding my professional network and allowing me to establish my own research line and laboratory.

Mots clés : oxidation pyrolysis chemical conversion biofuels experimental kinetic modelling fine particles polycyclic aromatic hydrocarbons flow reactor, chemkin pro pressure reaction pathways

Participants :
- Université de Saragosse (Espagne) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 181152.96 €
Projet B-REDiTx (Horizon Europe, appel à projets 2024)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Life Sciences
Statut du projet : SIGNED

Cancers are complex, continuously evolving diseases unique to each individual patient. Yet, most drugs are administered according to fixed, “one-size-fits-all” strategies that always aim to deliver the maximum tolerated dose until it fails due to toxicity or resistance. “Adaptive Cancer Therapy” (AT) is a novel approach which seeks to delay drug resistance by personalising when and how much drug is given, based on the tumour’s response dynamics. After initial success in prostate cancer, there are now important questions about which patients would benefit from AT, and how should we optimally adapt therapy? In this 2-year fellowship, I will develop a computational framework to: i) understand and track resistance evolution from a patient’s tumour burden data (volume, blood biomarkers, ctDNA), and ii) translate this knowledge into personalised dosing strategies to slow resistance evolution and improve quality-of-life. To predict whether a patient will benefit from AT we need a firm, quantitative understanding of the dynamics of resistance evolution. Under supervision of Prof Trevor Graham at the ICR, I will develop a software package (B-REDi) which integrates a family of mathematical models and Bayesian inference to learn about the evolutionary route to resistance in a patient. Subsequently, I will deploy this package to investigate how AT is altering resistance evolution in a clinical trial in ovarian cancer (ACTOv). Finally, I will use deep reinforcement learning to explore how our calibrated models can guide treatment decisions even under uncertainty about resistance mechanisms. In the future, I plan to establish my own lab to develop computational tools to mitigate resistance and toxicity through schedule personalisation. This project will serve me as a steppingstone towards this goal, and it will contribute novel tools and insights towards a future in which mathematical models are used akin to weather forecasts to inform clinical decision-making.

Mots clés : cancer drug resistance mathematical modeling ctdna mathematical oncology treatment scheduling

Participants :
- Institute of Cancer Research (Royaume-Uni) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 260347.92 €
Projet RESSAIG (Horizon Europe, appel à projets 2024)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Environment and Geosciences
Statut du projet : SIGNED

The RESSAIG project (Renewable Energy Site Selection using Artificial Intelligence and Geographic Information System) aims to enhance the identification and development of sites for clean renewable energy sources, such as photovoltaic (PV) arrays and both onshore and offshore wind farms. Utilizing a blend of Artificial Intelligence (AI), deep learning, remote sensing data, Geographic Information Systems (GIS), and government datasets, the project seeks to optimize site selection for renewable energy and water desalination necessary for hydrogen production.  Amidst the global shift towards sustainable energy solutions to combat climate change, there is an urgent need for more efficient and precise methods for identifying optimal sites for renewable energy installations. Traditional methods often overlook critical environmental, economic, and social factors that RESSAIG's integrative approach will consider, thus ensuring more sustainable and economically viable energy production. The urgency for sustainable and low-carbon energy solutions is accentuated by increasing environmental concerns and international commitments to reduce carbon footprints. RESSAIG's advanced mapping technologies come at a crucial time to meet the growing demand for renewable energy infrastructure and support the transition towards green hydrogen as a key energy carrier for the future. Our team brings together leading experts in AI, GIS, remote sensing, and renewable energy, uniquely positioning us to tackle this complex challenge. Our interdisciplinary approach combines cutting-edge technology with extensive field knowledge, enabling us to provide innovative solutions that are both scientifically rigorous and practically applicable. RESSAIG enhances renewable energy site selection, reducing costs and driving investment through detailed cartographies and economic analyses, fostering informed decisions and promoting sustainability, economic growth, and job creation.

Mots clés : renewable energy, site selection, geographic information system, remote sensing, multicriteria decision analysis, 

Participants :
- Université de Trente (Italie) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 209483.28 €
Projet TECH-LAB (Horizon Europe, appel à projets 2024)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Economic Sciences
Statut du projet : SUSPENDED

This project examines how technological changes, particularly robotics and AI, reshape labor markets and earning distribution in developing economies, with a focus on China. The study measures skills across three dimensions: education, task content, and occupation. By integrating diverse data sources—job vacancy data, industrial robot installations, AI patent records, customs data, and firm-level employee records—it aims to provide a nuanced understanding of technological impacts. The research will culminate in two papers: one using firm-level data and another utilizing Python-scraped job vacancy data, both offering comprehensive insights into labor market transformations. The first paper employs a Difference-in-Differences (DID) model, treating the “Made in China 2025” policy as a quasi-natural experiment to analyze how industrial policies influence skill structure and earning distribution. This analysis goes beyond the state of the art by developing a task-based skill index tailored to China’s labor market, avoiding biases from foreign data sources like the U.S. O*NET. The second paper focuses on AI’s impact on short-term labor demand, utilizing Bartik IVs to estimate the density of industrial robot installations at the city level. Innovatively, It integrates traditional human capital theory with emerging concepts of cognitive and non-cognitive skills. It will provide policymakers with actionable insights on the future of work, guiding decisions on labor regulations and education reforms. The interdisciplinary approach, merging economics, informatics, and sociology, ensures the findings are both scientifically rigorous and socially relevant. With open science practices, including data sharing and stakeholder engagement, the project promotes transparency and collaboration. It contributes to the broader understanding of how technological changes influence skill demand and earning dynamics, crucial for shaping future labor market policies in China and beyond.

Mots clés : robotics, ai, labor markets, skill structure, earning distribution, wage inequality, technological change, china, automation, workforce transformation, policy impact

Participants :
- Université de Trente (Italie) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 209483.28 €
Projet SE-HHLUI (Horizon Europe, appel à projets 2024)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Social Sciences and Humanities
Statut du projet : SIGNED

Thousands of kilometres of tunnels run under the streets of London, keeping waste contained and concealed. London inherited this sewerage system from the Victorians, most notably Chief Engineer Joseph Bazalgette, prompted by the Great Stink of 1858. While it has endured remarkably well, population growth means that tens of millions of tonnes of untreated sewage now overflow into the Thames every year, posing a serious environmental threat. This project moves beyond sewers as purely technical infrastructures to consider the relationship between public attitudes to waste and pollution, and the material dynamics of waste management from the Victorian era to the present day—a topic that is particularly relevant considering the growing effects of waste in the Anthropocene. Its main objective is to examine sewers as integral parts of London’s urban heritage, through the interdisciplinary lens of heritage studies, contemporary archaeology, ecocriticism, and urban studies, bringing them together within the scope of the environmental humanities as an ecology of sewers. Such an ecology can shed light on how infrastructures produce heritage legacies that engage humans, nonhumans, and the environment, and have ramifications for what it means to live in the contemporary city. In doing so, a form of urban infrastructural heritage that is concealed yet emergent can be unveiled. This knowledge can further aid cities in solving complex problems relating to global challenges such as clean waterways, climate adaptation, and infrastructure modernisation. Much like climate change itself, ecologies are difficult to envision and need to be actively sought out. A deeper understanding of London’s sewers, from the creation of its sewerage system to the present day, can address the question of what it means to live alongside these legacies, and demonstrate the relevance of crafting futures that acknowledge their role in our co-constructed, shared world.

Mots clés : ecocriticism, infrastructure

Participants :
- University College de Londres (Royaume-Uni) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 260347.92 €