Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : IDENTITY-ERROR (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Soci al World and Its Interactions
Entité participante : Université de Limerick — Pays : Irlande — Rôle : PI
Acronyme de l'entité : UL
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499093.0 €

Understanding how people discern the “truthfulness” of information is of fundamental importance to science. However, the complexity of dynamic information processing in social environments has made it convenient to “silo” research at different levels of analysis (neurological, psychological, social). To overcome this, we need end-to-end modeling that explains when and why people are vulnerable to falsehoods and mistruth. IDENTITY-ERROR pursues a new methodological framework that integrates a social-identity approach with recent advances in computational modeling to socially embed the emotional and cognitive elements of epistemic processing. I propose that the experienced “truthfulness” of information is determined by a recursive process from individual (affect, cognition) to social (identity-structured information). A process of social-self-evidencing fits evidence to models in an identity-congruent manner by error-tuning, either rejecting evidence (common) or models (less common). Identity-imbued models of reality manage error, and in some conditions, objectively incorrect models are very effective error sinks (i.e., they allow good evidence to be easily rejected). This breakthrough will be advanced by harnessing the identity structure of social information to make predictive processing models scalable at the social level. This allows the parallel computation of (i) information reliability alongside (ii) the social costs of accepting it as true (i.e., social evidencing). Importantly, error dynamics in these models enable the integration of emotional influence. IDENTITY-ERROR will establish a new field of psycho-social identity modeling, optimized for understanding the social functions of maintaining falsehoods and conspiracies, and providing a firm basis for socially grounded health and science communication that is resilient to conspiracy theory and misinformation.
Projet ERC : METAL (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe Sciences
Entité participante : Université de Tel Aviv — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Do massive stars undergo supernova explosions when collapsing into black holes? What mechanisms drove the Cosmos into an epoch of reionization? How was dust produced in the Early Universe? Scarcely any field of astronomy remains unaffected by massive stars: stars born with more than eight solar masses. Yet, studies in the Local Universe reveal substantial gaps in our understanding of massive stars related to mass loss, internal mixing, core-collapse, and stellar interactions. Uncertainties worsen at the low-metallicity conditions of the Early Universe. The primary reason for this: a severe lack of empirical constraints on massive binaries across the metallicity axis, driven by the rarity of massive stars and the shortage of adequate monitoring campaigns to study them. METAL leverages hundreds of hours worth of novel spectroscopic and interferometric data collected as PI using observatories such as the Very Large Telescope (VLT) and the Hubble Space Telescope, including a VLT Large Programme (116hr; 2023 - 2025). These campaigns monitor thousands of massive stars in our Galaxy and the Magellanic Clouds. Targeting unevolved OB-type stars, evolved Wolf-Rayet and Oe/Be stars, and elusive black holes, METAL will elucidate the initial conditions, evolution, and ultimate fates of massive stars at three metallicity anchors. Groundbreaking outcomes include (1) unprecedented statistics on the multiplicity, initial mass function, and structure of massive stars at low metallicity, (2) a tenfold increase in the sample of dormant black-hole binaries and the first such sample at low metallicity, and (3) a revised mass-loss prescription and a comprehensive binary characterisation of evolved massive stars at two metallicity anchors. The deliverables will be the defining calibrators for next-generation evolution models of massive stars, ushering in advancements in models of not only stellar evolution, but galaxy evolution, unresolved stellar populations, and core-collapse supernova.

Mots-clés : massive stars,spectroscopy,binaries,low metallicity,magellanic clouds,core collapse,black holes
Projet ERC : F-WORD (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Soci al World and Its Interactions
Entité participante : Université de Turin — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1114266.0 €

How and why does the “f-word” of fascism continue to constitute a compelling and urgent issue in contemporary Europe? F-WORD addresses this question through comparative ethnographic research among martial arts and combat sports sites as contexts for exploring how fascist practices and meanings are diffused and appropriated by youth. Using fascism as a heuristic device, this project also unfolds diachronically, seeking to explore how European history and memory politics are practiced in the daily lives of young people, and how they are signified and experienced, ultimately connecting to mythic representations of the past to inform a future-oriented fascist utopia. This will be achieved through a comparative ethnography in three European countries (Belgium, Italy, Poland), moving from combat sport and martial arts urban contexts to follow relations and life trajectories of young people (18-30) with the objective of mapping where youth encounter political discourses in non-political spaces. Extended ethnographic fieldwork (10 months) will be carried out by experienced post-docs supervised by the PI. Furthermore, the comparative design of the project aims to uncover the shared logic of fascism, not abstractly but as embedded ideological process. F-WORD proposes a new paradigm in the anthropological exploration of contemporary fascism, framed by the methodology of feminist ethnography, which will interrogate how specific methodological choices are involved in the process of knowledge- production and their epistemological consequences. The ground-breaking nature of this project is twofold: one, it unveils the world behind fascism, and thus the diffusion and various appropriations of fascist practices and meanings among European youth; two, it employs feminist ethnography as a paradigm which itself opens up new and unexplored directions of research.

Mots-clés : social anthropology,ethnography,fascism,neofascism,feminist ethnography
Projet ERC : RiboFriend (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Life Science
Panel : Molecules of Life: B iological  Mechanisms , Structures  and Functions
Entité participante : Technion - Institut de technologie d'Israël — Pays : Israël — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1412500.0 €

The majority of cellular proteins do not function alone; rather acting together to achieve concerted functions. Despite the prevalence of protein complexes, little is known of the mechanisms that ensure their correct folding and assembly in the crowded cytoplasm. The importance of the folding challenge is underscored by the growing number of misfolding diseases, often characterized by aggregation of lonely, unassembled protein-subunits. At the critical intersection of synthesis and folding, the ribosome is emerging as a hub, guiding the polypeptide-chain interactions with targeting factors, modifying enzymes and folding chaperones. We have recently discovered that even the final step of folding, the assembly into complexes, is coordinated with translation. To capture co-translational events, in vivo, we developed a ribosome profiling approach. This approach revealed that emerging polypeptide-chains are constantly engaged by their partner subunits, protecting them from misfolding (Shiber et al., Nature 2018). However the mechanisms regulating co-translational assembly pathways remain largely obscure. In this proposal, we aim to elucidate co-translational protein folding and degradation mechanisms, in health and disease. We will: (i) Identify and characterize novel co-translational degradation pathways, by targeting ribosomes synthesizing misfolding-prone subunits. (ii) Elucidate the conformational, energetic and kinetic parameters directing folding and assembly, at the atomic level, by molecular dynamics (iii) Develop RiboFriend a single-molecule in vivo approach to elucidate the interplay of chaperones, degradation and assembly factors, during synthesis. Our collective preliminary results strongly support the feasibility of this proposal. The ability to capture co-translational folding and misfolding pathways, in single-molecule resolution can revolutionize our understanding of conformational diseases and the aging process, opening new horizons for therapy.

Mots-clés : ribosome,co translational interactions,folding,misfolding,quality control,nascent chains,single molecule sequencing,nanopore,molecular dynamics,interface
Projet ERC : INWOOD (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Study of the Human Past
Entité participante : Université de Turin — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1489901.0 €

INWOOD studies industrialisation through the most important pre-industrial resource: wood. Scholarship on industrialisation has concentrated on the introduction and spread of new energy sources and materials, paying scant attention to the pre-existing resources. However, available data shows that wood was fundamental in the key sectors driving industrialisation, while its consumption increased during this process. INWOOD analyses the role that wood played during industrialisation and the impact of industrialisation on the actors and sectors most involved in wood use, with a focus on Europe during the first globalisation (1870-1914). Inspired by current debates on ecological crisis and energy transition, the project pioneers an interdisciplinary and multi-scale approach to analyse the links between the following interrelated issues: Economic dynamics on a macro scale: the changes in wood consumption in relation to industrial demands, but also the spread of alternative energy sources and materials; the transformation triggered by industrial technology in the geography of wood flows and in the logistics of the wood supply chain. Social dynamics on a micro scale: the impact of changes in the wood economy on the populations living close to the woodlands, in both the areas that were central before industrialisation and those that became central because of it; the choices made by actors involved in the exploitation of woodlands when faced with these developments. Ecological dynamics on multiple scales: the evolution of the European forest cover in terms of reforestation in some regions and deforestation in others; the main qualitative changes in the forest landscape in terms of tree species composition, age and density. INWOOD integrates sources and analytical methods to change the way scholars understand industrialisation and to provide the historical context for present-day debate about the role of forests in climate change mitigation and biodiversity preservation.

Mots-clés : environmental history history of energy timber frontier forest transition
Projet ERC : DEMALPS (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Study of the Human Past
Entité participante : Université de Turin — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1495320.0 €

DEMALPS studies late medieval mountain areas as the cradle of radically new political experiences, inspired by original values and practices of self-governance. The focus of the project is on the fourteenth- and fifteenth-century Western Alps, a broad territory (including modern French, Italian and Swiss regions) which saw unprecedented political unrest and experimentation with forms and institutions. This area was marked by numerous efforts at, and more than a few successes in, establishing autonomous collective governments. As a research opportunity, this branch of the Alps is characterised by an unparalleled abundance of exceptional rural sources for investigating this phenomenon: the registers of council proceedings, which reported the local assemblies’ composition, debates, and decisions over time. This rich corpus of sources, mostly scattered across local municipal archives, offers an extraordinary insight into the Alpine inhabitants’ political ideals and connections, with forms of government and practices of political participation vividly emerging from the registers. To explore such a large and diverse region, DEMALPS will adopt an interdisciplinary approach combining medieval history, digital humanities, diplomatics and archival science. The project will not only address questions about the values and practices of Alpine politics, but also provide innovative diplomatic and archival analysis of Alpine sources in comparative perspective. To this end, DEMALPS proposes to be the first native digital project specifically designed for collaborative work and sharing data in medieval history on this scale; research hypothesis, data, and their analysis will be made publicly available online during and after the project. The final goal of DEMALPS is to offer the scholarly community not only a new view of politics in late medieval Europe as they transformed into the modern institutions we know today, but also a research instrument to carry out further studies.

Mots-clés : digital humanities,archives,medieval sources
Projet ERC : GROWsmart (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed Matter Physics
Entité participante : Université de Tel Aviv — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Plants are multicellular organisms with no brain, yet they respond to external stimuli by coordinating their cells into strategic growth-driven movement, termed tropisms. Specifically, local sensory information is encoded, processed and propagated across the tissue via stochastic transport of growth hormones, and the plant responds by growing. Notably, information-processing and growth (actuation) are merged; thus plants are a unique case of morphological computation. However, they have yet to be studied from this perspective. Doing so could offer groundbreaking insights into distributed computation in physical and biological systems. This research aims to provide such insights, by identifying the physical principles governing how plants use stochastic transport of molecules to encode in memory and process sensory information, and coordinate optimal movement in a large number of cells, enabling complex navigation. I propose a 3-part multiscale study based on tropisms combining theory and experiments; I build on our recent findings, based on response theory, showing that wheat shoots’ tropic response depends on a history of stimuli-where shoots sum and subtract stimuli over different timescales. In Aim 1 (tissue level) I interpret memory as both a signal-processing and movement-control function, showing it is an emergent property ubiquitous in plants. I extract response functions from tropism experiments across species, organs and stimuli, and analyze them via signal-processing and control theory, identifying computational and movement-control capabilities. Aim 2 reveals the microscopic-level underpinnings of emergent memory; I will identify physical mechanisms relating stochastic properties of biological signaling, observed via live microscopy, to macroscopic responses. Aim 3 (organism level) will reveal how plants combine computation and movement control to solve navigational problems (e.g. gradient detection) and identify the physical limits of their capabilities.

Mots-clés : disordered systems,emergent memory,emergent computation,stochastic processes,response theory,signal processing,control theory,plant tropisms,auxin transport,behavior
Projet ERC : PLAnT (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Human Mobility, Environment, and Space
Entité participante : Université de Lisbonne — Pays : Portugal — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 452551.46 €

Planning of marine areas is being developed worldwide to support ocean sustainability. However, few existing marine spatial planning (MSP) initiatives consider climate change explicitly. This is a critical oversight in a rapidly changing world. At the same time, no formal MSP initiatives are envisioned for Antarctica; it is the only ocean basin globally without MSP under development. Antarctica is changing rapidly with potential repercussions around the planet, particularly for sea-level rise, climate regulation, and marine ecosystems functioning. Developing integrated, sustainable MSP for Antarctica is now more fundamental than ever. Excluding climate effects from MSP will lead to plans that are maladaptive and inefficient in sustaining marine ecosystems and their use under change. By contrast, if designed with climate objectives, MSP can provide substantial benefits while requiring few trade-offs. Efforts must be thus put together to effectively move towards climate-smart MSP in Antarctica and globally. Yet, contextual idiosyncrasies (political, social, economic) limit the effectiveness of such efforts. Understanding what challenges hinder the development of climate-smart MSP, in Antarctica and globally, and pointing ways to overcome such challenges is key. PLAnT will take up, for the very first time, such critical task. It will assess social-ecological vulnerabilities; build future scenarios; support area-based climate action and dynamic solutions; conduct governance analyses; and foster a paradigm shift on how to plan for sustainability and equity under change. Research on complex social-ecological systems rarely leads to one-size solutions, and there is a risk that solutions identified in PLAnT are context specific. Still, PLAnT will inspire nations worldwide to finding pathways and solutions to support ocean sustainability. At a time when the latter is firmly established in the international agenda, taking a leap forward on this topic is ground-breaking.

Mots-clés : marine spatial planning climate change antartica southern ocean sustainable ocean use marine conservation ocean policy ocean governance stakeholder perceptions social ecological systems dynamic management
Projet ERC : BabyMagnet (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Université de Tel Aviv — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1497669.0 €

Despite vast drug development efforts, brain tumors remain the leading cause of pediatric cancer deaths. Noninvasive monitoring of treatment response is crucial to reveal the mechanisms behind tumor-drug interactions and optimize patient care. However, standard magnetic resonance imaging (MRI) methods involve injecting metals, have severe difficulties in differentiating treatment response from tumor progression, are qualitative, and mandate prolonged anesthesia due to the lengthy acquisition. I propose to develop a transformative molecular MRI technology, based on the chemical exchange saturation transfer (CEST) contrast mechanism that will enable specific, quantitative, rapid, contrast-material free, treatment monitoring of pediatric brain cancer. Recently I revealed that a combination of mathematical CEST models and AI can generate quantitative biomarker maps of pH and protein concentration changes across the brain, two known hallmarks of cancer. Inspired by these results, I now propose to adopt a previously unconsidered perspective and to represent the underlying physics of CEST MRI as a computational graph, enabling an automatic AI-based optimization of molecular imaging. I hypothesize that the combination of biophysical models with a new AI framework, and their synergetic integration throughout the entire imaging pipeline will provide accurate noninvasive treatment monitoring. First, I will establish a method for automated optimization of MRI protocols for early determination of the tumor response to mainstream chemotherapy. Next, I will shorten the 3D scan time by an order of magnitude and quantify the response to next generation immunotherapy. Third, I will translate the method to clinical scanners and validate it in a human pediatric pilot study. This research will yield a fundamental understanding of the molecular mechanisms underlying treatment response and establish an innovative precision medicine methodology that will transform pediatric cancer imaging.

Mots-clés : medical imaging,machine learning,cancer imaging,treatment response,ai,mri,chemical exchange saturation transfer,molecular imaging,quantitative imaging,ph,car t cell,chemotherapy
Projet ERC : FoG (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Université de Tel Aviv — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1419375.0 €

Arguably, the most crucial objective of Learning Theory is to understand the basic notion of generalization: How can a learning agent infer from a finite amount of data to the whole population? Today's learning algorithms are poorly understood from that perspective. In particular, best practices, such as using highly overparameterized models to fit relatively few data, seem to be in almost contradiction to common wisdom, and classical models of learning seem to be incapable of explaining the impressive success of such algorithms. The objective of this proposal is to understand generalization in overparameterized models and understand the role of algorithms in learning. Toward this task, I will consider two mathematical models of learning that shed light on this fundamental problem.  The first model is the well-studied, yet only seemingly well-understood, model of Stochastic Convex optimization. My investigations, so far, provided a new picture that is much more complex than was previously known or assumed, regarding fundamental notions such as regularization, inductive bias as well as stability. These works show that even in this, simplistic setup of learning, understanding such fundamental principles may be a highly ambitious task. On the other hand, given the simplicity of the model, it seems that such an understanding is a prerequisite to any future model that will explain modern Machine Learning algorithms.  The second model considers a modern task of synthetic data generation. Synthetic data generation serves as an ideal model to further study the tension between concepts such as generalization and memorization. Here we with a challenge to model the question of generalization, and answer fundamental questions such as: when is synthetic data original and when is it a copy of the empirical data?

Mots-clés : stochastic convex optimization,statistical learning theory,theory of computer science,differential privacy