Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : BeyondRVE (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Materials Engineering
Entité participante : Université de Duisbourg et Essen — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1378301.0 €

As a result of the production process, many industrially relevant materials show a heterogeneity of their composition on a lower scale, leading to an uncertainty in their material properties. Traditionally, computational multiscale methods are built around the concept of Representative Volume Element (RVE), which seeks deterministic effective properties on cells which are sufficiently large. However, the concept of RVE is both too crude and too restrictive for today’s engineering requirements. The RVE concept is too crude because of its insensitivity to the material randomness, which is crucial for assessing the reliability of structures. The RVE concept is too restrictive because representative volumes need to be truly gigantic for certain multiscale materials, e.g., long-fiber reinforced thermoplastics (LFTs), precluding a computational treatment in reasonable time.  BeyondRVE introduces and studies microstructure-uncertainty quantifying volume elements (μQVEs), which account for the dispersion of the effective properties on cells of finite size. Me and my team will build up a groundbreaking multiscale methodology, taking into account the latest neural-network technology and screening the spurious boundary layers which arise for digital volume images of microstructures. Methodologically, BeyondRVE intends to provide a novel microscale solver which combines the efficiency of regular-grid methods and the accuracy of boundary-conforming meshes as well as fast and precise microstructure-generation tools for a variety of heterogeneous and composite materials.  Within BeyondRVE, these complementary pieces of simulation technology will be developed in an integrated and interdisciplinary fashion. Upon completion, a significant boost for the nonlinear mechanics of heterogeneous materials and lightweight design is expected, providing multiscale methods with more expressive results in shorter time for larger classes of materials with higher complexity.

Mots-clés : computational homgenization micromechanics representative volume element long fiber reinforced thermoplastics advanced high strength steels uncertainty quantification
Projet ERC : PolyFun (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : Université technique de Rhénanie-Westphalie à Aix-la-Chapelle — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Polymers are three-dimensional, flexible materials composed of macromolecular chains interconnected by crosslinks, which are indispensable in everyday products. Accurate computation of these materials is crucial to avoid trial-and-error synthesis, thereby extending polymer lifetimes and minimizing production waste. To date, continuum mechanics is the only discipline capable of achieving large-scale multi-physics computation of polymers. However, complex mechanical behaviors of polymers present significant challenges, necessitating the development of unconventional continuum theories and advanced machine learning techniques. While the former facilitates the capture of both local and nonlocal behaviors of polymers, the latter ensures model reliability by utilizing rich information from large datasets. Unfortunately, current machine learning approaches, when coupled with immature continuum theories, suffer from limited interpretability and an ad hoc nature. This underscores the need for a theoretically grounded machine learning approach to drive genuine advance in human knowledge. PolyFun aims to enable machine learning not only to recognize pattern, but also to develop a deeper understanding of data through the principles of continuum mechanics and polymer physics. This will be achieved by representing polymers as reproducing kernel Hilbert spaces and defining their structural and topological properties via these physical insights. PolyFun consists of two stages. Stage 1 focuses on learning from polymeric data and the domain knowledge of continuum and polymer mechanics. Stage 2 extrapolates understanding gained in Stage 1 to learn from real-world data and further enhance the computational scalability of the methodology. PolyFun’s unique perspective serves as a unified scientific language that bridges various disciplines (polymer physics, continuum mechanics and machine learning), fundamentally transforming the way we study and research in continuum interdisciplinary mechanics.

Mots-clés : continuum mechanics polymer physics mechanoluminescence damage strain induced crystallization phase transition reproducing kernel hilbert space machine learning
Projet ERC : EVOCATION (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Life Science
Panel : Molecules of Life: B iological  Mechanisms , Structures  and Functions
Entité participante : Université de Marbourg — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 773522.34 €

Almost all proteins perform their functions as part of protein complexes. These assemblies are intricate and often beautiful examples of evolution?s capacity to generate complexity. But are they built by natural selection? My recent work has revealed that they may in fact be produced and maintained across vast time scales by neutral processes, even if they provide no adaptive benefit at all. In this proposal, I will test this radical idea by bringing together ancestral sequence reconstruction and quantitative biochemistry of protein complexes. I will use this approach to experimentally unravel the evolutionary processes that generate and maintain biochemical complexity in three model systems that exemplify archetypical protein-protein interactions: In the first objective, I will unravel what drives the evolutionary gain and loss of homomeric interactions. I will recapitulate how the universally conserved enzyme citrate synthase repeatedly underwent changes in self-assembly state. I will test if these changes were adaptive or whether they result from new interfaces being highly evolvable. In the second objective, I will probe why protein complexes evolve to depend on transient interactions with folding and assembly chaperones. I will retrace how the CO2 fixing enzyme RubisCO acquired a set of dedicated assembly and folding chaperones. I will unravel whether the initial gain of new chaperone interactions was useful and determine what later caused RubisCO to start completely depending on them. In the third objective, I will unravel if many of the interactions between different complexes are caused by neutral evolution. I will use a new experimental approach in yeast to quantify the rate at which complexes gain interactions with the rest of the proteome by chance alone. Together, these experiments will show for the first time how adaptive evolution and neutral processes interacted to produce the intricate biochemical complexity we see inside cells today.

Mots-clés : protein evolution,ancestral sequence reconstruction
Projet ERC : LSS_BeyondAverage (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe Sciences
Entité participante : Université de Bielefeld — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1496955.0 €

The cosmic large-scale structure is entering a new ‘full space’ era with the launch of the Euclid galaxy survey satellite in 2023 coinciding with the first light for the Rubin Observatory. My proposal is centred on using galaxy surveys across a third of the sky as a laboratory for fundamental physics beyond the standard model by combining powerful theoretical models with exquisite data for galaxy clustering and weak gravitational lensing. To fully realise the potential of Euclid’s ‘full space’ galaxy survey, I will use my unique expertise in developing analytical, computational and statistical methods to predict and analyse the cosmic large-scale structure beyond the average. I will hunt for fundamental physics by going beyond the average probed by standard two-point analyses and unlocking additional information from novel clustering statistics sensitive to different density environments. I will provide new insights into dark matter dynamics and enable a field-based analysis by pioneering a particle-wave duality inspired phase-space technique that goes beyond the average effective fluid modelling. This will push forward modelling to a new level, overcome limitations of standard fluid and N-body methods, and enable the joint modelling of dark matter, baryons and massive neutrinos.  My research program will unlock key insights into particle physics (by pinning down the total neutrino mass and potential wavelike properties of dark matter), the early universe (by constraining primordial non-Gaussianity providing hints for inflationary physics), gravity (by measuring the nature of dark energy and potential modifications of general relativity) and astrophysics (by determining the relationship between galaxies and dark matter). My expertise in developing analytical, computational and statistical methods for clustering dynamics and statistics and my leadership within the Euclid collaboration places me in a strategic position to lead such an ambitious research program.

Mots-clés : late time cosmology,dark matter,gravitational clustering,galaxy surveys,galaxy clustering statistics,weak gravitational lensing statistics,cosmic structure formation,large scale structure,dark energy,modified gravity,massive neutrinos,primordial non gaussianity
Projet ERC : GYNVEPI (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Human Mobility, Environment, and Space
Entité participante : Université de Bielefeld — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1384403.0 €

GYNVEPI will lay the foundations of an epidemiology of gynaecological violence (GV). GV refers to sub-optimal experiences of care that can be perceived as abusive or dehumanising (e.g. non-consented medical acts, discrimination). Understood as a structural and gendered form of violence, it is a phenomenon that is yet to be fully defined and investigated: in contrast to the neighbouring field of obstetric violence, it has not been researched. Taking Germany as an exemplary setting, GYNVEPI aims at understanding the who, the when, the how, and the health consequences of women’s experiences of GV. Through an innovative, intersectional, gender-expansive and gender-transformative approach to social epidemiology, it will provide a definition, a framework and a quantitative, validated instrument for the study of GV. The mixed-methods (reviews, workshops, interviews, survey, digital platform) and interdisciplinary approach ensure the depth and strength of the findings. GYNVEPI is important: gynaecological care constitutes in many countries the first point of contact for women’s health. Women are exposed throughout their life to the risk of GV, and its toll is assumed to be large, with consequences in the short- to long-term on mental, physical, social and sexual health. GYNVEPI is timely: the momentum of obstetric violence research provides a good starting point to reflect on GV; testimonies of GV outside the scientific arena urge researchers to grasp the topic; digital tools and participation enable to collect effectively and quickly experiences of care users, including among marginalised groups. GYNVEPI will generate new knowledge on GV and its associations with health. Its gender-transformative component will initiate a transformation of harmful gender norms among the researchers and “researched” through reciprocal empowerment. Lastly, the methodological approach will be applicable in further studies, e.g. longitudinal studies and GV studies in Europe.

Mots-clés : social epidemiology,gynaecological care,women s health,gender
Projet ERC : PEP (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Compl exity
Entité participante : Université Justus-Liebig de Gießen — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1484625.0 €

In the cognitive and neural sciences, the brain is widely viewed as a predictive system. On this view, the brain conceives the world by comparing sensory inputs to internally generated models of what the world should look like. Despite this emphasis on internal models, their key properties are not well understood. We currently do not know what exactly is contained in our internal models and how these contents vary systematically across individuals. In the absence of suitable methods for assessing the contents of internal models, the predictive brain has essentially remained a black box.  Here, we develop a novel approach for opening this black box. Focusing on natural vision, we will use creative drawing methods to characterize internal models. Through the careful analysis of drawings of real-world scenes, we will distill out the contents of individual people’s internal models. These insights will form the basis for a comprehensive cognitive, neural, and computational investigation of natural vision on the individual level: First, we will establish how individual differences in the contents of internal models explain the efficiency of scene vision, on the behavioral and neural levels. Second, we will harness variations in people’s drawings to determine the critical features of internal models that guide scene vision. Third, we will enrich the currently best deep learning models of vision with information about internal models to obtain computational predictions for individual scene perception. Finally, we will systematically investigate how individual differences in internal models mimic idiosyncrasies in visual and linguistic experience, functional brain architecture, and scene exploration.  Our project will illuminate natural vision from a new angle – starting from a characterization of individual people’s internal models of the world. Through this change of perspective, we can make true progress in understanding what exactly is predicted in the predictive brain.

Mots-clés : predictive processing,internal models,natural vision,scene perception,visual cortex,drawing,fmri,eeg,deep neural network models,representational similarity analysis
Projet ERC : WOWMA (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : Leuphana University of Lüneburg — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499721.0 €

Tropical islands are often stigmatized as tax havens. But only a few tropical islands are important exporters of financial services. This is surprising since many of them feature attributes associated with tax havenry. They are small, sovereign, reachable from major financial centers within a few hours, and practice British common law. Why have so few applied tax havenry as a development strategy, nonetheless? Conventional wisdom suggests that political stability, often measured with contemporary indicators of good governance, makes the difference. The perception of political stability among foreign investors is, however, interwoven with racist biases. Previous research shows that tropical islands had to emphasize their British heritage, essentially a code for whiteness, to attract foreign capital during decolonization. WOWMA’s objective is thus to explain why some tropical island jurisdictions were better able than others to convey stability through an image of whiteness in this historical period. In contrast to previous research emphasizing the rule of law, WOWMA investigates the hypothesis that the absence of income taxes paired with white control over government distinguished emerging tax havens from other tropical islands. To this end, WOWMA (1) proposes a new theory on the emergence of tax havenry in the Global South, linking an island’s economic structure under colonial rule to the absence of income taxes and the persistence of white oligarchy; (2) applies causal inference methods on original historical data gathered from archival sources; and (3) probes the persistence of racist biases in investment decisions through conjoint experiments. The project provides unprecedented depth in the study of small island states’ political and economic development. It foregrounds the entanglement of racism with perceptions of political stability and develops new strategies for its measurement.

Mots-clés : political economy,political institutions,fiscal capacity,racism,colonialism,taxation,microstates
Projet ERC : LEVER (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Human Mobility, Environment, and Space
Entité participante : University of Kassel — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1222194.0 €

Grand and persistent social-ecological challenges, such as climate change, push forward a rapidly growing discourse on how sustainability science can support society in dealing with today’s global crises. Between studying change and contributing to change, sustainability science seeks both to analytically understand sustainability problems, but also to design interventions that can contribute solution-options to these problems. However, knowledge about how to intervene in order to reach a desirable vision (i.e., transformation knowledge) is typically missing. Especially how to deliberately engage with values as places of intervention (leverage points), as proposed by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, remains a critical knowledge gap. To fill these gaps, LEVER aims to critically develop and apply a transformative theory and practice to support the (co-)production of transformation knowledge, including a focus on transformation knowledge necessary to investigate and unleash values as leverage points for sustainability transformation. To this end, LEVER originally combines transdisciplinary research with the empirically rigorous methods of social science experiments. The scientific breakthrough consists in delivering an integrated theory of transformative research, capable of representing the many co-evolving links between its design principles, philosophy of science, normative assumptions, and ethical dimensions. Methodologically, LEVER opens new avenues on how to produce transformation knowledge, by experimenting with, testing and evaluating novel transformative methods. Finally, LEVER seeks to answer the globally relevant and timely question of how to unleash values. Using a treatment and control group experimental design, LEVER assesses the impact of values-targeting interventions and demonstrates the potential of values as leverage points. LEVER pioneers a salient conscious science-society relationship in Europe.

Mots-clés : transdisciplinary co production of knowledge,sustainability transformation,leverage points,values,peri urban,rural urban
Projet ERC : Civic Finance (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : Université de Glasgow — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1496084.0 €

Time is running out for humanity to reduce carbon dioxide (CO2) emissions in order to avoid catastrophic climate change yet the financial system continues to enable unsustainable levels of CO2 emissions in many carbon-intensive sectors, including the fossil fuel industry. Law can help govern this problem but effective legal interventions need to take into account the complexity of the financial system. The overarching objective of the Civic Finance project is to develop a complexity-informed theory of law and finance that can underpin the legal foundations of a civic financial system; in particular, a financial system that refrains from enabling unsustainable levels of CO2 emissions in the fossil fuel industry. To do that, the project will develop a theoretical framework that synthesises institutionalist theories of law and finance with studies of complexity in the financial system. It will then combine doctrinal and socio-legal methodologies to collect empirical data on the legal rules underpinning the European financial system’s continued support of unsustainable levels of CO2 emissions in the fossil fuel industry. To test the effectiveness of these legal rules, the project will develop an agent-based model of the European financial system that will simulate their potential impact on CO2 emissions in the fossil fuel industry. The project will then integrate this simulation data and the empirical data into the theoretical framework to develop a complexity-informed theory of law and finance. Building on institutionalist theories of law and finance, the project argues for the utility of complexity-sensitive methods to study how law shapes complex financial systems. In the longer term, the project will revolutionise how European lawmakers, financial institutions, and civil society organisations use the law to mitigate CO2 emissions enabled by the financial system.

Mots-clés : complexity,legal thoery of finance,social ecology,agent based modelling