Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : ROSE (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Life Science
Panel : Environmental B iology, Ecology  and E volution
Entité participante : Université de Cologne — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1498000.0 €

Novel traits can provide fitness advantages and drive evolution. New specialised metabolites are traits that arose in different plant lineages, and even replaced existing metabolites. Why, and how almost universal metabolic pathways can be replaced in entire groups of species by biochemically distinct pathways without persistence of any species producing both metabolites remains unknown. Components of gene regulatory networks that control such pathways might act as regulatory switches that flip between pathways.  The replacement of the widely conserved red pigment anthocyanin by the biochemically distinct betalains offers a unique opportunity to understand the integration of novel traits into existing regulatory systems. Notably, no plant species producing both anthocyanins and betalains has yet been identified, but both show similar environmental responses. The unification of systems biology with population and molecular genomics allows to elucidate the role of gene regulatory networks and regulatory switches in metabolite evolution.  Given the great importance of understanding evolutionary innovation and the potential use for metabolic engineering, our work promises to be groundbreaking and have profound impact on many different fields of evolutionary and genetic research. Specifically, our work plan includes the following aims: I. Identify regulatory networks and switches that enabled the exchange of metabolic pathways II. Analyze the fitness consequences of reciprocal pathway exchange and regulatory switching III. Reveal the short-term selection consequences of metabolites during the domestication of food crops  Understanding how metabolic pathways can be exchanged in plants will provide insights into the important embedding of evolutionary innovation into existing systems with potential practical applications for crop improvement and bio-engineering.

Mots-clés : specialised metabolites,gene regulatory network,plant evolution,domestication
Projet ERC : OdiN (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and Communication Engineering
Entité participante : Université de Cologne — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Neurodegenerative diseases (NDs) affect millions worldwide and have complex causes, including genetic predisposition, environmental factors, drug use, and aging. Currently, there is no cure for most NDs, with treatments mostly aiming to alleviate symptoms and slow disease progression. In general, for any potential therapy, the preservation of neurons and their function—known as neuroprotection—should be a priority. The effectiveness of current therapies, including deep brain stimulation in case of Parkinson’s disease (PD), is limited due to their lack of spatial precision. The OdiN project aims to develop a flexible, implantable device that enables both neurostimulation and monitoring of neuronal activity. It leverages organic electronics technology, combining near-infrared (NIR) OLEDs to induce photobiomodulation (PBM) for neuroprotection and an OLED–organic photodiode system for detecting neuronal activity. This bidirectional device will be miniaturised to a few hundred micrometres and tested in vitro in a neuronal model of PD, where it will study the effects of PBM on neuroprotection. The device will also be implanted in a PD mouse model to assess its benefits for preventing neurodegeneration in vivo. A technical extension will explore scaling to multi-site stimulation arrays to achieve spatially controlled PBM. The OdiN project is positioned in the emerging fields of NIR OLED development and PBM, introducing a unique and unprecedented method for precisely targeting neurons without the need for genetic modification. This innovation not only simplifies experimental protocols in animal models but also opens the door to clinical applications that are far more accessible and ethically less problematic than current optogenetic techniques. It holds tremendous promise for advancing the understanding and ultimately the treatment of NDs, offering a tuneable, scalable and minimally invasive implant technology that could revolutionise therapeutic strategies in neuroscience.

Mots-clés : high brightness oleds,near infrared emission,high detectivity organic photodiode,polariton filter,brain machine interface,photobiomodulation,neuroprotection,neurodegenerative diseases,calcium imaging,haemodynamics
Projet ERC : MAGNETIC (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : Université Bocconi — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1420400.0 €

The goal of this research proposal is to advance the calculus of variations of area in codimension higher than one, specifically existence and regularity of its critical points (minimal submanifolds) and properties of its gradient flow (mean curvature flow). These are central objects in mathematics since three centuries and contributed to the birth of geometric analysis, geometric measure theory, and calculus of variations. Their (non-)existence often reveals deep links between small-scale geometry (curvature) and large-scale structure (topology). While the hypersurface case is by now well understood, with several deep results in the last two decades, very little is known in codimension at least two, especially for unstable submanifolds not minimizing area. Several projects will focus on the intimate link between area and some well-known physical energies: phase transitions are understood to give diffuse approximations of hypersurfaces, while vortices in models of superconductivity relate to codimension two submanifolds. An energy proposed by me and D. Stern in this context is the abelian Higgs model, which I plan to use to extend the Lagrangian mean curvature flow past singularities and to relate stability and regularity of minimal submanifolds, which are two long-standing questions in geometric analysis (among other projects), by exploiting the much richer structure given by the PDEs solved by critical points of this energy. I will also look at candidates in codimension three and higher, inspired by energies from gauge theory and others of Ginzburg–Landau type, relating stability and minimality in critical dimension and attacking other basic open questions. Finally, I will also work on another set of projects exploiting parametrized varifolds, a variational object pioneered by me and T. Rivière combining advantages of the parametrized and intrinsic viewpoints, to study Lagrangian surfaces and minimal submanifolds of higher dimension

Mots-clés : minimal surfaces,geometric measure theory,mean curvature flow,abelian higgs model,parametrized varifolds,ginzburg landau
Projet ERC : ExpPov (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Soci al World and Its Interactions
Entité participante : Université Bocconi — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1393425.0 €

The reduction of poverty is of societal importance. After advances in the study of poverty in the late 20th century, however, most high-income countries have settled on an annual measure of household income as their primary measure of poverty. In contrast, ExpPov argues that shifting socio-economic conditions in recent decades necessitate a new conceptual approach for understanding poverty and a new methodological framework for the collection of data related to the experience of poverty. Specifically, ExpPov confronts four central tenants of poverty research. First, in contrast to national or regional investigations of poverty, ExpPov proposes that the geographic distribution of poverty across neighbourhoods within a municipality is an understudied, yet important source of stratification that directly affects well-being. Second, this project expands measurement of the dimensions of poverty to incorporate physical and subjective well-being, confronting the income-specific approach that dominates the field. Third, ExpPov measures the dynamics of poverty throughout the year to understand the sources and consequences of month-to-month volatility in poverty. Fourth, in contrast to the practice of identifying poverty as an outcome of the 'family, market, and state', this project proposes that the dynamics, dimensions, and geographic distribution of poverty are direct sources of the more-fractured family structures, precarious jobs, and tenuous welfare states that define post-industrial economies. From a data perspective, ExpPov introduces tools to produce low-cost panel surveys with the ability to target respondents in particular postal codes and working for specific employers, such as Uber or Deliveroo. Methodologically, ExpPov introduces a framework to evaluate the contextual effects of poverty on the core institutions of social life. Conceptually, ExpPov will reset the field's understanding of the social mechanisms through which poverty is experienced and reproduced.

Mots-clés : poverty,social policy,labour markets,welfare states,families
Projet ERC : PrSc-HDBayLe (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : Université Bocconi — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1488673.0 €

As the scale and complexity of available data increase, developing rigorous understanding of the computational properties of statistical procedures has become a key scientific priority of our century. In line with such priority, this project develops a mathematical theory of computational scalability for Bayesian learning methods, with a focus on extremely popular high-dimensional and hierarchical models.  Unlike most recent literature, we will integrate computational and statistical aspects in the analysis of Bayesian learning algorithms, providing novel insight into the interaction between commonly used model structures and fitting algorithms. Key methodological breakthroughs will include a novel connection between computational algorithms for hierarchical models and random walks on the associated graphical models; the use of statistical asymptotics to derive computational scalability statements; and novel understanding of the computational implications of model misspecification and data heterogeneity.  We will derive a broad collection of results for popular Bayesian computation algorithms, especially Markov chain Monte Carlo ones, in a variety of modeling frameworks, such as random-effect, shrinkage, hierarchical and nonparametric ones. These are routinely used for various statistical tasks, such as multilevel regression, factor analysis and variable selection in various disciplines ranging from political science to genomics. Our theoretical results will have direct implications on the design of novel and more scalable computational schemes, as well as on the optimization of existing ones. Focus will be given to develop algorithms with provably linear overall cost both in the number of datapoints and unknown parameters. The above contributions will dramatically reduce the gap between theory and practice in Bayesian computation and allow to fully benefit of the huge potential of the Bayesian paradigm.

Mots-clés : bayesian computation,markov chain monte carlo,hierarchical models
Projet ERC : Frailife (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Human Mobility, Environment, and Space
Entité participante : Université Bocconi — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1401938.0 €

Across the European Union, approximately 4% among individuals under age 16 have a disability, and over 15 million school-age children are known to have special educational needs. Disabilities limit children in their everyday activities and impact families in myriad ways. FRAILIFE aims to comprehensively analyze the effects of a child disability on family members – parents, siblings, and grandparents – in Europe, using high-quality data (registers, surveys, social media), innovative research designs and methods. By focusing on the impact of child disability on family members’ life course trajectories, health outcomes, and social participation, FRAILIFE speaks to both general family processes and the special case of Europe’s frailest families. The existing literature on families with a disabled child is scattered, built on small convenience samples, and often focused on a specific disease; the findings are inconclusive and non-generalizable. FRAILIFE elevates the evidentiary standard by: i) using a population approach, which compares families with and without a disabled child and provides opportunities to uncover heterogeneous effects; ii) going beyond simple associations between child disability and family outcomes to establish causal pathways, identify underlying mechanisms, and uncover causal effects; iii) investigating both short- and long-term consequences of child disability on families; iv) adopting a comparative perspective to uncover the moderating effects of institutions and culture. Beyond the specific insights about the networked consequences of disability, this project contributes to the field of family demography, as the analysis of extremely frail families has relevance for the general functioning of all families facing adverse events. FRAILIFE will bring families with a disabled child out of their invisibility, offering new and important insights on their functioning, characteristics, and challenges, while informing effective family-centered policy

Mots-clés : family,child disability,life course,demography
Projet ERC : ExcelAM (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : Université technique de Munich — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1484926.0 €

Additive Manufacturing (AM) by Laser Powder Bed Fusion (LPBF) has the potential to revolutionize future product development, design and supply chains. Since the underlying multi-scale physics are not well understood, its potential can presently not be exploited. Sub-optimal process conditions lead to severe defects on different scales, rendering parts unsuitable for use. Critically, known regimes of stable processing go along with very low built rates, i.e., very high costs compared to other processes. This limits LPBF to selected high value applications such as medical devices but prohibits applications in mass production where it otherwise could allow for entirely new technologies. ExcelAM aims at the digital discovery of novel high-throughput process regimes in LPBF, to increase build rates by at least one order of magnitude. Computational modeling would be perfectly suited for this purpose since it allows to observe physics that are not accessible to measurement and to study novel process technologies that are not feasible with existing hardware. Unfortunately, existing computational tools are by far not powerful enough, given the complexity of LPBF. Therefore, ExcelAM will develop novel game-changing methodologies, grouped into two main classes: First, novel high-fidelity multi-physics models will be developed, capturing the complex multi-scale nature of LPBF. These are combined with cutting-edge high performance computing schemes, allowing for predictions on unprecedented time spans and system sizes. Second, novel data-based learning approaches will be developed to enrich the physical models with process data, while exploiting the manifold of existing data as effective as possible. Based on these cutting-edge tools, ExcelAM will push the limits of LPBF. Moreover, by making them publicly available, ExcelAM will help scientists and practitioners in the field of production engineering and beyond to face the technological challenges of the 21st century.

Mots-clés : metal additive manufacturing by laser powder bed fusion,physics based modeling and simulation,computational mechanics,coupled multi physics problems,high performance computing
Projet ERC : SURFACE (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : École polytechnique de Bari — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499750.0 €

Macroscopic adhesion is of utmost importance in key technologies such as soft and climbing robots, aerospace grasping technologies, human-robot interactions, pick-and-place manipulators. Commonly, bioinspired adhesives interfaces have been characterized from a quasi-static perspective, neglecting the effect of dynamic excitations. Nevertheless, recent observations suggest that added micro-vibrations may be exploited to strongly enhance and rapidly tune macroscopic adhesion. By exploiting the multiplicative coupling between geometric- and viscoelastic vibration-induced enhancements of macroscopic adhesion, SURFACE aims at designing future soft interfaces with unprecedented and tuneable adhesion strength. To this end, I aim to: (i) develop highly efficient numerical tools for studying adhesion of patterned soft surfaces under micro-vibration excitation, (ii) unveil the coupling effect between topography and viscoelasticity that determine the interfacial strength and toughness (iii) design optimal surface topography and excitation for macroscopic adhesion tuning, by exploiting artificial intelligence models to unveil new mechanisms for adhesion enhancement, (iv) prove the adhesive performance reached, by experimentally testing high-resolution 3D printed interfaces with the desired topography and superposed micro-vibrations. So far, the adhesive performance of bioinspired patterned interfaces has been limited by manufacturing capabilities at the micro/nanoscale. SURFACE ground-breaking approach aims at exploiting dynamics excitation to outperform state-of-the-art adhesive interfaces. By exploiting artificial intelligence models, SURFACE aims at revealing new mechanisms for adhesion enhancement, which lay beyond our intuition. Rapidly tuneable strong adhesive interfaces have the potential to revolutionize cutting-edge technologies based on soft adhesive interfaces that require to move and place objects quickly and with accuracy.

Mots-clés : surface adhesion patterned surfaces dynamic excitation
Projet ERC : MACHIRO (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and Analytical Chemical Sciences
Entité participante : École polytechnique fédérale de Lausanne — Pays : Suisse — Rôle : PI
Acronyme de l'entité : EPFL
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2173004.0 €

Chiral solution-processable semiconductors are a new class of materials that could revolutionize optoelectronics by enabling circularly polarized light (CPL) emission and spin control without external magnetic fields at room temperature. This could enhance the efficiency of displays, solar cells, holography, cryptography, and biomedical imaging, while opening new possibilities in quantum information processing. However, current trial-and-error approaches, limited by the absence of suitable techniques to probe mechanisms, prevent systematic breakthroughs in designing chiral materials with strong CPL emission and spin polarization.  MACHIRO will overcome these limitations. We will develop the first ultrafast broadband magneto-chiroptical microscope to map the Stokes vector of light polarization emitted from chiral materials with diffraction-limited spatial and femtosecond-temporal resolution. This will enable us to directly track the interplay between charge, spin, and light polarization in time and space, to mechanistically understand how CPL and spin polarization arise from structural chirality imprint on the excited state.  We will use this new tool to study a range of i) heavy-element containing chiral halide perovskite semiconductors, as well as ii) purely molecular twisted materials with much less spin orbit coupling. We will systematically vary dimensionality (3D bulk to 0D quantum dots), hierarchical level of chirality, helical pitch and addition magnetic impurities, while always monitoring the role of local disorder through mapping. Thus, we will identify dominant loss channels and arrive at a unified mechanism of how light and spin polarization are connected.  Finally, we will integrate insights across all materials, establishing universal design rules for using chirality to control spin and light simultaneously, enabling next-generation solution-processable spin-optoelectronic technologies with unprecedented energy efficiency.

Mots-clés : optoelectronics,chirality,ultrafast spectroscopy,halide perovskites,molecular electronics,chiral materials,ciss
Projet ERC : DIAL (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : École polytechnique fédérale de Lausanne — Pays : Suisse — Rôle : PI
Acronyme de l'entité : EPFL
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2329985.0 €

Liquid-vapor phase change is essential in power generation, heating and cooling, thermal desalination, and membrane distillation. With the advancements enabled by micro/nanoengineering of phase change devices, the interfacial transport resistance becomes increasingly important, dictating the ultimate performance limit. However, fundamental understanding of the interfacial process remains elusive, particularly regarding the interface accommodation coefficients (IACs), which quantify the probability of evaporation and condensation events at the molecular level. Characterizing IACs has been a long-standing challenge, primarily due to the lack of experimental designs sensitive enough to molecule-interface interactions and capable of distinguishing various contributing factors.  The DIAL project aims to demystify the complexities surrounding IACs arising from three key factors – surface charges, interface contamination, and nanoconfinement. Here, we decouple these largely overlooked or insufficiently studied factors by designing a series of targeted experiments, leveraging IR and confocal Raman microscopy for combined thermometry and chemical analysis. With evaporating droplets in vacuum, we establish the baseline for IACs first on pure and then on charged and contaminated fluids. Meanwhile, to investigate the effect associated with phase change devices, we characterize contaminant migration and the corresponding impact on evaporation from membranes with microscale pores and at the same time, build a multiscale model relating IACs to device performance. Finally, with evaporation from sparsely spaced nanotube arrays, we examine the nanoconfinement effect on IACs, overcoming the optical diffraction limit.  By isolating or minimizing specific effects in each configuration, our experiments collectively advance the fundamental understanding of liquid-vapor interfacial transport while offering practical design insights for all interface-sensitive energy and water systems.

Mots-clés : evaporation coefficient,condensation coefficient,liquid vapor phase change,surface charge,contamination,nanoconfinement