Data-Gouv-ML/pcri-actions-marie-curie-participations-et-projets-laureats huggingface dataset:
Projet NeMo (Horizon Europe, appel à projets 2024)
Action : Postdoctoral Fellowships - Global Fellowships
Panel : Physics
Statut du projet : SIGNED

The manipulation of the electromagnetic environment of molecular systems provides a way to control their properties and dynamics. Specifically, under strong coupling conditions, the formation of hybrid light-matter states delocalized over a large number of molecules has been shown to change the rate of chemical reactions and modify diverse transport phenomena. Despite considerable theoretical and experimental efforts, the ultimate physical mechanism responsible for these effects is not well-understood, mainly because of the complexity of these systems. Molecules present multiple internal degrees of freedom, which makes their theoretical description unmanageable when dealing with macroscopically large ensemble sizes. Besides, the phenomenology when entering the ultrastrong coupling regime is largely unexplored, where new theoretical challenges come into play. The objective of this action is to provide the missing theoretical and computational frameworks to study the dynamics of realistic molecular systems ultrastrongly coupled to a complex electromagnetic environment. Starting from the full multimode cavity QED Hamiltonian, we will first derive the effective model for such physical settings including effects that have been missed so far but are nonetheless important to address the observed (as well as the accessible) phenomenology. Then, we will apply many-body numerical techniques from quantum optics to describe the dynamics of strongly-driven, ultrastrongly-coupled systems. With these tools at hand, we will study nonequilibrium effects in these platforms, specifically energy transport and driven-dissipative phase transitions. The outcomes of this project will thus bring us closer to the definite understanding of the phenomena afforded by molecular strong coupling, and allow us to reliably predict nonequilibrium effects relying on molecular ultrastrong light-matter coupling.

Mots clés : cavity qed, polaritonic chemistry, strong coupling, ultrastrong coupling, molecular polaritons, cavity induced mechanisms, energy transfer, plasmonic nanocavities, light matter interactions

Participants :
- Université technique de Munich (Allemagne) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 424992.0 €
- University of California System (Etats-Unis) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
Projet SHyCOV (Horizon Europe, appel à projets 2024)
Action : Postdoctoral Fellowships - Global Fellowships
Panel : Information Science and Engineering
Statut du projet : SIGNED

Hydrogen energy is a promising component of a sustainable future, though its extensive adoption is challenged by issues in storage and transportation. Composite Overwrapped Pressure Vessels (COPVs) have emerged as a promising solution due to their superior strength-to-weight ratio and corrosion resistance. However, maintaining the structural integrity of COPVs under the demanding conditions of hydrogen storage remains a major concern. Micro-damages and operational impacts can compromise the safety and lifespan of these vessels, yet there is currently no standardized EU-wide methodology for in-service condition assessment.  The SHyCOV project (Structural Health Monitoring of Hydrogen Composite Overwrapped Vessels) addresses this critical gap by developing an advanced Structural Health Monitoring (SHM) system tailored for COPVs. This project is timely given the anticipated growth of hydrogen infrastructure in Europe and the crucial role COPVs will play in this transition. SHyCOV employs a multi-disciplinary approach, integrating advanced ultrasonic wave propagation modeling, experimental testing, and innovative sensor technologies to detect and localize damage within COPVs. The outcome will be a cost-effective, real-time monitoring system that extends the lifespan of hydrogen storage vessels and prevents potential catastrophic failures.  By bridging a significant gap in current EU standards and ensuring the safe deployment of hydrogen energy, SHyCOV will enhance scientific knowledge and facilitate the broader adoption of hydrogen as a clean energy source. The project also provides researcher with essential skills in assessing structural damage in hydrogen composite vessels, positioning him as a leader in this innovative field. SHyCOV’s SHM solution holds considerable commercial potential, supporting future industrial applications and aligning with global initiatives to reduce carbon emissions and transition to sustainable energy.

Mots clés : composite pressure vessels, structural health monitoring, non destructive testing, hydrogen storage safety

Participants :
- Faber industrie spa (Italie) — Partner, associated partner, Organismes privés (hors enseignement), subvention : 0.0 €
- Université de Bologne (Italie) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 473121.9 €
- Federal Institute For Materials Research and Testing (Allemagne) — Partner, associated partner, Recherche, subvention : 0.0 €
- University of California System (Etats-Unis) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
Projet PopGenAI (Horizon Europe, appel à projets 2024)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Life Sciences
Statut du projet : SIGNED

Understanding the demographic history of populations and species is essential, as it plays a critical role in conservation and breeding programmes for both wild and domesticated species. However, inferring demographic history from genetic data faces the problem of model non-identifiability, where different demographic histories fit the genetic data equally well. This issue is amplified by the use of summary statistics instead of complete genetic data. Addressing the model non-identifiability problem is fundamental for improving the precision of demographic inference, particularly in complex cases involving multiple independent admixture events or barriers to gene flow.  This project aims to resolve the problem of model non-identifiability in demographic inference by developing a generative artificial intelligence (AI) tool that can uncover histories that are indistinguishable when a particular summary statistic is used. This generative AI will serve three core purposes: (1) identify non-identifiable demographic histories, helping researchers better understand the range of possible historical events that could explain the data; (2) apply this tool to clarify complex demographic histories in domesticated species, such as cattle, providing more accurate insights into their genetic background and informing breeding programmes to maintain genetic diversity; and (3) detect barriers to gene flow in recently diverged species, offering deeper insights into local adaptation and speciation processes valuable for conservation.  By addressing the model non-identifiability problem and applying this approach to key biological questions, this project has the potential to transform the study of demographic history in population genetics and open new perspectives for research on species evolution and adaptation.

Mots clés : demographic inference, model non identifiability problem, generative artificial intelligence, machine learning, admixture history, domesticated species, genetic barrier to gene flow, local adaptation

Participants :
- Université d'Édimbourg (Royaume-Uni) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 276187.92 €
- University of California System (Etats-Unis) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
Projet riskADAPT (Horizon Europe, appel à projets 2022)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Environment and Geosciences
Statut du projet : SIGNED

The evolutionary relevance of behavioral traits has been largely debated among biologists. Behavioral changes often constitute the first response to changing environmental conditions. This suggests that behavioral variation among individuals within a population potentially represents the raw material for natural selection, ultimately determining the ecological and evolutionary responses to new selective pressures. However, the genetic basis of behavioral traits remains largely unknown. This uncertainty limits our understanding of the role of behavior in shaping the adaptive potential of natural populations. In this project I aim to uncover the molecular underpinnings of risk-taking behavior, a key trait associated with survival in the lizard Anolis sagrei under new predation regimes. First, using restriction site-associated DNA sequencing (RADseq) data, I will characterize the heritability of risk-taking behavior by estimating its additive genetic variance based on multigenerational pedigrees of lizards that have undergone behavioral assessments. I will then search for genomic regions and variants associated with risk-taking behavior by FST outlier scans, selective sweep scans and genome-wide association study (GWAS) with the use of RADseq and whole-genome sequencing (WGS) data. Finally, using whole-genome bisulfite sequencing (WGBS) data, I will explore the impact of DNA methylation on the variation in risk-taking behavior to shed light on the role of epigenetic mechanisms in controlling animal behavior. By characterizing the genetic and epigenetic architecture of risk-taking behavior, riskADAPT will decisively advance our understanding of the role of ecologically-relevant behaviors in the evolutionary adaptation to rapid environmental changes.

Mots clés : behavioral evolution, animal personality, rapid environmental change, novel predator, genomics, epigenomics, anolis

Participants :
- CERCA Institution (Espagne) — Coordinator, beneficiary, Recherche, subvention : 181152.96 €
- Université de Princeton (Etats-Unis) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
Projet FLASH (Horizon Europe, appel à projets 2021)
Action : Postdoctoral Fellowships - Global Fellowships
Panel : Information Science and Engineering
Statut du projet : CLOSED

The FLASH project aims to establish the theoretical foundations of machine learning and wireless communications that will enable the vision of assisted and self-driving systems. Unfortunately, current systems cannot provide safe and reliable driving because they lack distributed and real-time learning algorithms meeting the critical latency and reliability requirements in highly dynamic and fast-varying wireless channels. Although the fifth generation of cellular systems supports the communication demands for assisted and self-driving, and machine learning proposes federated learning for distributed scenarios, the wireless communications and machine learning domains are not sufficiently integrated for real-time critical applications. Yet, it is only by their integration that the vision of assisted and self-driving will become real. To this end, we will establish a theoretical and algorithmic integration of federated learning and cellular networks that serve vehicles, which we refer to as federated learning supporting efficient and reliable inference over vehicular networks (FLASH). FLASH builds on the co-design of a fundamentally new ecosystem in which federated learning algorithms address critical constraints from vehicular applications, while resource allocation algorithms adapt wireless communication resources to the inference tasks. The project will implement FLASH by establishing and validating theoretical and algorithmic foundations for assisted and self-driving systems. Thus, we not only expect to have an academic impact but also a great societal impact by enabling the fulfilment of sustainable development goals through reduced fuel consumption, traffic emissions, and fatalities. Ultimately, the project provides outstanding training for a talented young researcher, Dr. Mairton Barros, at Princeton University for 24 months with Prof. H. Vincent Poor, and KTH Royal Institute of Technology for 12 months with Prof. Carlo Fischione.

Mots clés : federated learning, vehicular networks, reliability, low latency, inference, autonomous driving, safety prediction, object detection

Participants :
- KTH Institut royal de technologie (Suède) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 305928.0 €
- Université de Princeton (Etats-Unis) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
Projet NITRODEOX (Horizon Europe, appel à projets 2021)
Action : Postdoctoral Fellowships - Global Fellowships
Panel : Environment and Geosciences
Statut du projet : SIGNED

The decline in oxygen content (deoxygenation) in the ocean is one of the major features of global change, and affects biological processes and the biogeochemical cycling of key elements such as nitrogen (N). Oxygen minimum zones (OMZs) are intensifying and expanding their distribution, and many coastal waters such as estuaries and enclosed seas are experiencing seasonal anoxia events. OMZs and coastal waters are ecosystems with global relevance for N processing and hot spots for nitrous oxide (N2O) cycling. N2O is a major ozone-depleting substance and a potent greenhouse gas, and its microbial production depends on N and oxygen availability. So far, many studies have addressed the importance of environmental drivers in N and N2O cycling. However they are incomplete predictors of ecosystem functioning. Contrarily, little attention has been paid to biotic community associations, despite they can exert selective pressure on marine microbes and affect ecosystem functioning. NITRODEOX aims to investigate the changes in microbial communities caused by ocean deoxygenation, and to assess how these changes affect N cycling and N2O production. The applicant will develop a novel and interdisciplinary approach combining the quantification of N transformation rates using 15N labeling incubations, with the latest advances in bioinformatics analysis of deep metagenomic sequencing, and network inference methods in order to connect microbial community structure with ecosystem functioning. This project will cover the three major oxygen minimum zones, and two coastal sites (the Chesapeake Bay, and the Mar Menor coastal lagoon), and builds on the advantage of already having an exceptional collection of samples from past cruises. NITRODEOX represents a novel step to identify key taxa and reveal hidden microbial community controls on N and N2O cycling, which is crucial in view of the expansion and intensification of OMZs, and the increasing human impact on coastal waters.

Mots clés : nitrogen cycle, nitrous oxide, n2o, ocean, deoxygenation, oxygen minimum zones, coastal waters, network, microbial community, metagenomics, metagenome assembled genomes, bioinformatics

Participants :
- Conseil supérieur de la recherche scientifique (Espagne) — Coordinator, beneficiary, Recherche, subvention : 261380.64 €
- Université de Princeton (Etats-Unis) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
Projet Trained_Lung (Horizon Europe, appel à projets 2023)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Life Sciences
Statut du projet : SIGNED

The world health organisation has labeled inflammatory diseases the greatest threat to human health. Inflammation is a major cause of pathogenesis in respiratory conditions such as bronchiectasis, ARDS, and COPD. There is a growing appreciation that our prior pathogen exposure can induce trained immunity in macrophages. Trained_Lung will explore how bacterial pneumonia induces central trained immunity and how this can lead to excessive inflammation and lung damage, in response to other bacteria or environmental pollutants. Recently it has been shown that repeat exposure to influenza can lead to lung damage due to the altered functionality of lung macrophages, leading to excessive inflammation and damage to the structure of the lung. However, it remains unclear if this is the case for bacterial pneumonia and if it can lead to inappropriate inflammatory response upon rechallenge with other bacterial species or environmental pollutants. I propose that bacterial pneumonia will drive enhanced myelopoiesis and enhance circulating monocyte inflammatory responses. Further, these circulating monocytes will replace resident alveolar macrophage populations over time, seeding the lung with these trained monocytes which will become resident alveolar macrophages. Alveolar macrophages are constantly in contact with a range of bacteria, viruses, PAMPs, and pollutants which all have the potential to drive excessive inflammatory responses. Trained_Lung will identify pathogens that can train human macrophages and examine secondary inflammatory responses to a range of bacterial pathogens and environmental pollutants. Further, myelopoietic changes in the bone marrow and functional changes in macrophages post-pneumonia in mice will be explored, and how these changes impact subsequent challenges in terms of lung function and damage. Finally, Trained_Lung will explore how trained macrophages impact immune responses within iPSC-derived human lung organoids, adding a key translational aspect

Mots clés : trained immunity, pneumonia

Participants :
- Université Justus-Liebig de Gießen (Allemagne) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 173847.36 €
Projet TICI-SLE (Horizon Europe, appel à projets 2023)
Action : Postdoctoral Fellowships - Global Fellowships
Panel : Life Sciences
Statut du projet : SIGNED

Systemic Lupus erythematosus (SLE) is an autoimmune disease with a complex pathogenesis. Immune complexes (ICs) are formed and deposited in various tissues, leading to inflammation and damage. T cells are part of the adaptive immune system, but also express innate-like receptors that could interact with ICs by binding complement factors or the constant region of immunoglobulins. However, the extent of these interactions and there relevance for the SLE pathogenesis has not been investigated so far. In the TICI-SLE project, we will analyze these interactions by 1) investigating the expression of receptors that could interact with ICs on T cells in the blood and inflamed tissue of SLE patients, 2) cultivating T cells with immune complexes and antibodies stimulating or blocking individual receptors, thereby elucidating the consequences of these interactions and 3) analyzing spatial T cell/IC interactions and their secondary consequences (like proinflammatory changes in stromal cells and chemoattraction of inflammatory cells) in skin samples from SLE patients. We hypothesize that T cell/IC interactions result in (co-)stimulation of T cells, leading to production of proinflammatory cytokines, cytotoxicity and induction of an inflammatory microenviornment. Understanding these interactions can then be exploited by the future development of therapeutics targeting these interactions. An additional focus of the project will be the personal development of the applicant and the dissemination and exploitation of project results.

Mots clés : systemic lupus erythematosus, autoimmunity, t lymphocytes, immune complexes, complement system

Participants :
- Hôpital universitaire de la Charité de Berlin (Allemagne) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 265647.84 €
- Université Harvard (Etats-Unis) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
Projet NeuroSelfTickle (Horizon Europe, appel à projets 2024)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Social Sciences and Humanities
Statut du projet : SIGNED

In perception, neural responses are never identical, even to the same sensory stimuli. This variability is particularly evident in the somatosensory domain: why does the same tactile input feel ticklish when applied by others but not by ourselves? The phenomenon of self-tickle cancellation has intrigued philosophers, biologists, and psychologists for centuries. Motor control theories propose that self-generated movements elicit top-down predictions in the cerebellum that suppress bottom-up tactile input in the somatosensory cortex. However, how these top-down and bottom-up signals interact in the somatosensory cortex – and particularly across its different layers – to cancel self-tickle is unclear. NeuroSelfTickle aims to unravel these complexities with advanced electroencephalography, psychophysical modelling and laminar magnetoencephalography. The project seeks to identify neural markers of tickle sensation, map layer-specific cortical activity in top-down and bottom-up signal integration, and apply these insights to understand the neurocognitive mechanisms of self-tickle cancellation. This interdisciplinary approach combines well-established motor control theories with cutting-edge neuroimaging techniques and state-of-the-art haptic robotics, allowing for highly controlled yet naturalistic self-touch stimulation. The findings are expected to provide fundamental insights into how self-generated predictions influence neural computations and enhance our understanding of brain disorders where self-tickle cancellation fails, such as schizophrenia and autism. NeuroSelfTickle will create a synergy between the applicant's expertise in somatosensory neural oscillations and excitability fluctuations, and the host lab's pioneering research on tickle perception and self-tickle cancellation. The Donders Institute, along with Dr. Kilteni's lab, will provide the necessary theoretical knowledge in sensorimotor control, as well as technical expertise and support in neuroimaging.

Mots clés : somatosensory perception, neural variability, motor control, eeg, meg, layer specific cortical dynamics

Participants :
- Université Radboud de Nimègue (Pays-Bas) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 232916.16 €