Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : PHAST (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth System Science
Entité participante : Université Louis-et-Maximilien de Munich — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : LMU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1210393.0 €

Ice streams are river-like corridors of fast flowing ice that account for the vast majority of ice discharge to the ocean in continental ice sheets. Their most outstanding feature is that they can appear spontaneously within a slowly moving ice sheet, self-organize in evenly spaced patterns, and switch on and off over time. Yet, a full explanation of ice stream formation and evolution is one of the longest standing open problems in glaciology. This knowledge gap has precluded fundamental investigations on the role of ice streams in driving ice sheet change, and also casts doubt on the ability of state-of-the-art ice sheet simulation codes to project future sea levels. Recent work by the PI identified an entirely novel class of feedbacks related to the onset of basal sliding which could be the missing ingredient needed to fully explain observed ice stream dynamic behaviors. Drawing on a unique combination of novel glaciological observations, first-principle theoretical work, and ice-sheet-wide numerical simulations, PHAST builds on this insight to: 1. Develop a complete and coherent theory of ice stream spatial and temporal dynamics that clarifies under what environmental conditions these dynamics are to be expected.  2. Test the hypothesis that sliding onset physics play a central role in ice stream dynamics through a targeted field experiment.  3. Build a new ice sheet simulation code that resolves sliding onset and ice stream margin physics at low computational cost thanks to emerging domain partitioning methods.  4. Assess for the first time the sensitivity of Antarctic and Greenland mass loss to ice stream dynamics. By developing new modeling tools and generating process-level understanding across scales ranging from few tens of meters to thousands of kilometers, PHAST will lead the way towards unraveling the complex interplay between internal ice sheet dynamics and climate over timescales ranging from decades to millennia.

Mots-clés : ice streams,mathematical modeling,theoretical glaciology,basal sliding,ice mechanics fieldwork,ice sheet modeling
Projet ERC : noDisease (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems E ngineering
Entité participante : Université de Cologne — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499668.0 €

Plant pathogens cause devastating diseases that result in yield reduction and substantial economic losses. Chemical control is often ineffective, costly, and raises ecological concerns. An alternative strategy for generating durable chemical-free disease resistance would have a considerable impact on food security. Most plants species are naturally resistant against various pathogens species. This phenomenon is referred to as non-host resistance (NHR). For example, wheat has durable NHR to the powdery mildew fungus Blumeria hordei (Bh) that normally infects susceptible barley. Employment of NHR genes in susceptible host plants has the exciting potential to provide durable pathogen resistance. Yet, the understanding of molecular NHR mechanisms is limited and the isolation of NHR genes using conventional approaches is restricted by genetic incompatibilities between host and non-host species. Given the growing importance of crop diseases, addressing this critical knowledge gap is both ground-breaking and timely, contributing to food security and promoting non-chemical pest resistance.  To bypass the bottlenecks common to standard procedures, 'noDisease' will use an innovative strategy, combining high-throughput molecular and biochemical methods whose developments I have pioneered to rapidly isolate the molecular factors underlying wheat NHR to Bh. This strategy will enable prediction of the most durable NHR genes for effective disease resistance. Specifically, our work plan includes the following aims:  I. Isolate the genetic determinants that underly NHR II. Transfer NHR genes between plant species III. Assess the potential for NHR genes to provide durable disease resistance.  This ambitious and timely approach employed within ‘noDisease’ will resolve long-standing bottlenecks that hinder molecular-genetic research on NHR, and has the potential to revolutionize both the basic understanding and deployment of non-chemical disease resistance in agriculture.

Mots-clés : food security,fungal pathogens,cereals,durable disease resistance,pathogen recognition
Projet ERC : POLDEI (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Texts and Concepts
Entité participante : MF École norvégienne de Théologie, Religion et Société — Pays : Norvège — Rôle : PI
Acronyme de l'entité : MF
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Political deification—i.e. the transformation to and treatment of political leaders as divine beings—occurs in various religious and national contexts in Asia and has been understood as instances of messianism, custodianship, sacred kingship, martyrdom, pacification, and charismatic authority among others. These concepts travel, intersect, and imbue the different political systems in Asian countries. Yet they have been studied only as discrete political phenomena, not as related and overlapping concepts under an overarching theory that explains the deification of political leaders. POLDEI will build a transdisciplinary theory of political deification by using case studies from East, South-East, and South Asian countries to analyse popular cultural concepts that address the deification of political leaders. This transdisciplinary project will use innovative methods from film studies, visual/performance studies, and culture studies, in addition to the study of religion, political science, and anthropology, to interpret the popular understanding of such religiopolitical phenomena. The project POLDEI will develop a theory of political deification to understand how the legitimacy of political rule is grounded in concepts derived from religious worldviews. This theory built from Asia is not just for Asia. The proposed theory will provide us with the analytical tools and conceptual categories we need to decode the contemporary challenge of rising religious nationalisms not just in Asia but at the global level. From the perspective of the history of science, the project will test the results of this radical act of relocating not just the ground of theory but also the geopolitical pattern of applicability.

Mots-clés : religion and politics,political theology,religious nationalism,theory from the global south,popular culture,asian studies
Projet ERC : CLIMATE-edGE (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Human Mobility, Environment, and Space
Entité participante : Libera Università Internazionale degli Studi Sociali — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1434800.0 €

Proposal Summary: The private sector plays a crucial yet complex role in the climate transition. While responsible for a significant portion of global greenhouse gas emissions, it also holds vast investment capital and innovative potential. CLIMATE-edGE will address a critical question: Under what conditions do private sector climate commitments translate into measurable actions, and what factors determine whether firms and sectors are effectively aligning their climate promises with actual performance? By examining publicly traded firms in the EU/EEA, the UK, and the US, the research will uncover complex relationships between climate commitments (climate talk) and climate performance (climate walk), in turn reflecting on the impact of regulatoryThe private sector plays a crucial yet complex role in the climate transition. While responsible for a significant portion of global greenhouse gas emissions, it also holds vast investment capital and innovative potential. CLIMATE-edGE will address a critical question: Under what conditions do private sector climate commitments translate into measurable actions, and what factors determine whether firms and sectors are effectively aligning their climate promises with actual performance? By examining publicly traded firms in the EU/EEA, the UK, and the US, the research will uncover complex relationships between climate commitments (climate talk) and climate performance (climate walk), in turn reflecting on the impact of regulatory measures aimed at advancing climate efforts in the green economy. This project will break from traditional approaches by developing a novel database which will contain the full universe of climate commitment and performance data; it will then apply data transformation techniques to enhance interoperability of these data and, finally, it will conduct both advanced econometric and machine-learning analyses to uncover hitherto hidden and latent patterns in the private sector’s climate talk-walk efforts.

Mots-clés : machine learning,private sector climate transition,climate commitment,climate performance,climate innovation,climate finance
Projet ERC : BigBayesUQ (Horizon Europe, appel 2021, 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 : 1492750.0 €

Recent years have seen a rapid increase in available information. This has created an urgent need for fast statistical and machine learning methods that can scale up to big data sets. Standard approaches, including the now routinely used Bayesian methods, are becoming computationally infeasible, especially in complex models with many parameters and large data sizes. A variety of algorithms have been proposed to speed up these procedures, but these are typically black box methods with very limited theoretical support. In fact empirical evidence shows the potentially bad performance of such methods. This is especially concerning in real-world applications, e.g. in medicine. In this project I shall open up the black box and provide a theory for scalable Bayesian methods combining recent, state-of-the-art techniques from Bayesian nonparametrics, empirical process theory, and machine learning. I focus on two very important classes of scalable techniques: variational and distributed Bayes. I shall establish guarantees, but also limitations, of these procedures for estimating the parameter of interest, and for quantifying the corresponding uncertainty, within a framework that will also convince outside of the Bayesian paradigm. As a result, scalable Bayesian techniques will have more accurate performance, and also better acceptance by a wider community of scientists and practitioners. The proposed research, although motivated by real world problems, is of a mathematical nature. In the analysis I consider mathematical models, which are routinely used in various fields (e.g. high-dimensional linear and logistic regressions are the work horses in econometrics or genetics). My theoretical results will provide principled new insights that can be used, for instance in multiple specific applications I am involved in, including developing novel statistical methods for understanding fundamental questions in cosmology and the early detection of dementia using multiple data sources.

Mots-clés : asymptotics,frequentist validation of bayesian methods,posterior consistency,uncertainty quantification,adaptation,nonparametric bayes,variational inference,theoretical guarantees for computational schemes
Projet ERC : NEMESIS (Horizon Europe, appel 2023, 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 : 1498794.0 €

Europol defines criminal networks as a modern version of the Hydra mythological creature, with obscure genesis, fluid structure and multifaceted evolutions. Leveraging a unique synergy between Social Sciences and Statistics, NEMESIS departs from current practice to translate the main obstacles (i.e., data incompleteness and complexity) underlying the reconstruction, discovery and disruption of these modern criminal networks into a unique resource for crucially expanding available knowledge and policies. This ambitious perspective builds on three somewhat overlooked, yet fundamental, observations. First, data missingness in criminal networks can be, in fact, a fundamental source of information on the structure and secrecy strategies of organized crime, and, as such, should be embraced and not disregarded for network reconstruction. Second, the complexity and multiplexity of covert and overt interactions among criminals may hide a wealth of yet unexplored knowledge on the formation, mutation and sociogenesis of criminal networks, hence invaluable for their discovery. Third, “cutting” Hydra heads without realistic predictions of the corresponding re-generation processes undermines not only the development of innovative law-enforcement disruption strategies, but also the identification of more nuanced high-value targets. NEMESIS transforms these perspectives into rigorous, interpretable and practically-impactful generative statistical models of criminal networks and introduces innovative concepts of secret criminal species, covert networking activity codes and dark evolutionary trees. This perspective is expected to revolutionize current practice of conceptualizing, reconstructing, analyzing and disrupting criminal networks, while opening new avenues for the creation of frontier data-related resources, social theories, law-enforcement strategies and public policies.

Mots-clés : social network analysis,computational social sciences,quantitative criminology
Projet ERC : CITE (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and Analytical Chemical Sciences
Entité participante : Université technique de Munich — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2481640.0 €

Small molecule anti-cancerSmall molecule anti-cancer therapies generally have a well understood mechanism of action. Still, they are frequently plagued by poor efficacy and high rates of treatment resistance, especially in the context of pancreatic cancer. One promising newly recognised route of therapy resistance is intratumoral drug inactivation through members of the tumour microenvironment (TME) such as the tumour-associated microbiome. With the CITE project, I aim to develop novel analytical technologies that enable us to study drug metabolism by the TME in situ and in isolation from systemic drug metabolism and provide a novel platform for selectively studying the contribution of the TME to drug resistance mechanisms. . I will deploy a spatial pharmacometabolomics approach using Laser Desorption Rapid Evaporative Ionisation Mass Spectrometry (LD-REIMS). First, a novel and sensitive source setup including laser optics and high efficiency aerosol transfer will be developed, followed by instrumental setups for imaging applications at (sub)cellular resolution, high throughput acquisition of cell lines and thick tissue section imaging. Subsequently, a spectral database of abundant (tumour, endothelial cells, fibroblasts etc) and rare cell types (immune cells, microbiome) in the TME will be created to enable cell-type specific assignment of drug delivery and drug metabolism in mass spectrometry imaging datasets and validated using imaging mass cytometry. I will develop a organotypic culture (3D-OTC) model of pancreatic cancer to study isolated, intratumoral drug metabolism in a controlled environment while still using relevant clinical material. We will use pancreatic cancer from a genetically engineered mouse as model system to develop our novel analytical technologies and to assess intratumoral metabolism of small molecule cytotoxics and targeted chemotherapies used to treat pancreatic cancer. Lastly, these findings will be translated to tissues of human origin.

Mots-clés : drug metabolism,tumour microenvironment,mass spectrometry imaging
Projet ERC : PARTIALJUSTICE (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Soci al World and Its Interactions
Entité participante : Université technique de Munich — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1472390.0 €

AI comes with great promises, but more critically, it comes with demonstrated harms, from racial and gender bias, to discrimination, wrongful arrests, defamation, surveillance, or the extractive labor and practices required for its creation. I contend that the most concerning aspect of AI is the exacerbation of existing structural inequalities and creation of new ones. Two prevailing responses have emerged to the social and ethical problems of AI: ethicists and social scientists have focused largely on principle-based approaches, and developers have focused on technical fixes for addressing problems in AI models. This project speaks to an urgent gap between these approaches: it centers the experiences of marginalized communities and develops interventions into AI concerns that are designed by and for those communities. This project develops the novel approach of participatory algorithmic justice. Participatory algorithmic justice defines a concept and standards of practice for collaborative research to better understand who and what AI harms, but also how these harms should be redressed. The project investigates how economic, cultural, and political harms from AI are experienced by structurally marginalized groups through multi-sited, intersectional ethnographic fieldwork. This fieldwork informs participatory design workshops to develop specific interventions into problems identified by research collaborators. This research is brought to life through an innovative combination of graphic storytelling and a public-facing mapping platform. Participatory algorithmic justice brings the voices, priorities, and concerns of those affected by AI to the forefront of debates over what kinds of AI people want to live with. Tackling a critical problem of global technology justice, this project is a crucial intervention for communities, researchers, and developers to redress AI harms.

Mots-clés : ethnography,artificial intelligence,justice,inequality,ethics,participatory research,graphic art
Projet ERC : NTasePro (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Life Science
Panel : Molecules of Life: B iological  Mechanisms , Structures  and Functions
Entité participante : Université technique de Munich — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1755873.0 €

Survival of a cell depends on its ability to receive and process information coming from the environment. Second messengers are the key transmitters of information that rapidly amplify the signal inside the cell. Cyclic nucleotides (CNs) are the most diversified category of second messengers and are found in all organisms modulating diverse pathways. CNs are best studied in bacteria, where they have a variety of biological functions. In humans, the only described cyclic di-nucleotide is cGAMP, which elicits an antiviral immune response. cGAMP is rapidly emerging as a promising drug candidate in e.g., cancer immunotherapy. I hypothesise that additional CNs contribute to yet uncharacterized pathways in humans and my aim is to discover novel CNs and their signalling pathways. CNs are synthesized by nucleotidyltransferases (NTases) and many human NTases remain uncharacterized by the lack of activity in the absence of the cognate ligand.  This project aims to reveal the molecular function of uncharacterized NTases and to discover novel CN signalling pathways in humans in immune and non-immune-related areas of biology. I will focus in Objective ❶ on the activation of as yet uncharacterized NTases and in Objective ❷ I want to identify CN signalling pathways involved in immune responses and beyond. The proposed experiments will provide me with candidates and precedence for CN signalling in humans other than cGAMP. The concept that yet uncharacterized CNs are involved in immune signalling as well as other pathways is highly innovative. Together, the well-studied activities in bacteria and the overall conservation of NTases in humans, further support fundamental involvement of CN signalling in humans. My proposed research will open up conceptually new modes of regulation in human biology, reveal novel signalling pathways and in the long-term contribute to new therapeutic options using CNs as drugs.

Mots-clés : nucleotide based second messengers,cgamp,cgas sting,nucleotidyltransferases,signal transduction,innate immunity,cyclic nucleotides
Projet ERC : PhosphotoSupraChem (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Université de Fribourg-en-Brisgau — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499063.0 €

Phosphates and phosphate esters underpin biological information transfer, signal transduction, and contribute to the energetics of life. Biochemical fuels that include phosphates (ATP, GTP) drive selective processes, by incorporating chemical information (A vs. G) in their fuel structure. Despite the multifaceted role of phosphates in biology, their use in supramolecular systems chemistry remains largely underexplored. PhosphotoSupraChem develops roles for phosphates outside of biology and capitalizes on the idea of providing chemical information within abiotic phosphate fuels to control selectivity and reactivity in the context of fuel-driven structure formation. The information is provided by chemical functionalization of energy-rich phosphate fuels, whereby the information encode structural assembly of fuels prior to their consumption, or transfer large chemical groups onto self-assembling species during energy transfer. Specific aims are: (i) Understand the chemical design space for phosphate fuel self-assembly and how such structures control selectivity and reactivity when transferring the energy for the formation of transient assemblies; (ii) Construct fuels, capable of transferring structural information, enabling folding and chimeric dissipative self-assembly; (iii) Promote waste-driven fuel formation, substrate release and peptide bond formation (v) Involve fuels in automated synthesis for microfluidics materials discovery PhosphotoSupraChem pioneers an unexplored area of science at the roots of dissipative systems and phosphate-driven systems chemistry. This strategy profoundly contrasts the present non-equilibrium self-assembly research featuring fuelling with molecules that lack structural and recognition elements. In the long term, fabricating biocompatible dissipative systems and developing automated platforms that guide fuels to specific functions will be a decisive step toward creating dynamic assemblies in vivo and programming active soft matter.

Mots-clés : chemical reaction networks,systems chemistry,dissipative self assembly,peptide self assembly,