Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : ASPAC (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Université d'Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499957.0 €

When making decisions, autonomous agents rely on their own incentives and private information. When these factors diverge, conflicts can arise, leading to inefficiencies or suboptimal outcomes. Economists have studied this phenomenon under the principal-agent framework, where the principal seeks to coordinate the agents so that the agents' rational choices align with the principal's objectives. This framework is gaining increasing attention, especially in the era of AI, where automated agents become more capable of making decisions autonomously.  In computer science, algorithmic approaches to principal-agent coordination strategies have become an area of growing interest, giving rise to influential subfields, such as algorithmic information design, contract design, and automated mechanism design. Despite this progress, much of the literature focuses on static, one-shot models, which fall short of capturing the complexities of real-world problems occurring in stochastic environments. These problems are inherently dynamic and involve sequential decision-making. Solving them requires addressing fundamentally new computational challenges arising from the interplay between the complexities of the principal-agent model and the dynamics and stochasticity of the environment.  This project aims to tackle these challenges by investigating the computational and algorithmic foundations of stochastic principal-agent coordination (SPAC). The goal is to introduce a useful framework for formalising SPAC and develop algorithms and computational theories regarding the tractability, approximability, and learnability of problems within the framework. By exploring these fundamental aspects, we expect the project to substantially advance the theoretical literature while contributing algorithmic solutions to real-world applications of SPAC.

Mots-clés : principal agent problem,sequential decision making,stochastic games,stackelberg games,information design,bayesian persuasion,contract design,automated mechanism design,optimal commitment,computational complexity
Projet ERC : Volute (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Université d'Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2143785.0 €

Volute is an ambitious project in the space of Computer Vision and Machine Learning. While these research domains have made tremendous progress in recent years and have contributed to the success of many tech companies, we are still seeing the highest impact of artificial intelligence (AI) in the natural language processing (NLP) space - for example, in products such as ChatGPT or AI-assisted search on Google and Bing. This success stems from a "foundation model" (e.g., GPT) that is then fine-tuned with hand-labelled instructions to become an assistive chatbot. While many models in Computer Vision are named foundation models, none of them have been able to produce the same impact. The main challenge in Computer Vision is the complexity of the input and output space. NLP tasks are text-based; in contrast, the range of representations in computer vision is endless: images, videos, depth, infrared, medical data, satellites, and more. At the same time, the outputs are similarly diverse: images, text, discrete labels, pixel annotations, bounding boxes, 3D reconstruction, audio, and more. These outputs are often not necessarily meant for human consumption but are the inputs for other systems, such as autonomous driving or robotics. It is thus unrealistic to expect to find an actual foundation model in computer vision. Volute aims to find a new universal model type: an "omniversal trainer", which acts as a data generator to train task-specific models. The omniversal trainer uses a generator, trained on large data collections, and thus learns priors about the world, such as physics, appearance, and visual diversity, but does not need specific task knowledge. A downstream-task model can then be trained with limited task-specific data, augmented with the world knowledge of the universal trainer. This drastically reduces the labelling effort, both in time and cost, for downstream applications and will have a significant impact as it poses a paradigm shift in Computer Vision.

Mots-clés : computer vision,synthetic data generation,generative models,foundation models
Projet ERC : DISCOS (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Université d'Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499768.0 €

Considering that all life on Earth ultimately relies on the interaction of carbon-based organic matter with light through photosynthesis, it is unsurprising that organic molecules which absorb and emit light broadly overarch scientific disciplines, with great implications in modern technologies related to sensing, bioscience, electronics and energy. Organic “superluminophores” (SLs) (J-aggregates) afford a unique synergy of properties, with fundamental advantages over traditional dyes – they absorb light more strongly and emit light more quickly and efficiently. Their sharp absorption and emission spectra also endow high colour purity and are desirable for selective and efficient excitation/ energy transfer in complex systems. SLs are hence very promising, both for facilitating new technologies, and for solving persistent issues faced by classical organics. However, current materials are critically limited by a need for supramolecular self-assembly. SL ensembles are formed, driven by interactions BETWEEN multiple molecules. Self-assembly is highly sensitive to changes in molecular structure, and dependent on both concentration and the local environment. This restricts predictable and reproducible processing and excludes SLs from applications where a low concentration/ discreet species is required. Here we aim to solve this longstanding limitation and propel SLs to the forefront. Firstly, systematic structural chemistry will afford robust design rules to construct Discreet Organic Superluminophores (DISCOS) that harness interactions WITHIN rigid molecules to enhance how they absorb and emit light, precluding self-assembly. Taking advantage of this fundamental advance, the DISCOS will next be broadly colour tuned to develop bespoke materials aimed at solving two of the most persistent applied problems facing luminescent organics – the instability of blue organic light emitting diodes, and the low luminescence efficiency of near-infrared dyes.

Mots-clés : organic materials,conjugated organic materials,organic semiconductors,optoelectronics,organic fluorescence,organic phosphorescence,organic luminescence,organic light emitting diodes oleds,organic photovoltaics opv
Projet ERC : OCI (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed Matter Physics
Entité participante : Institute of Science and Technology Austria — Pays : Autriche — Rôle : PI
Acronyme de l'entité : ISTA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1831500.0 €

Topological electronic phases manifest fascinating phenomena, including electronic transport via topologically-protected edge states, anomalous responses to external fields, and excitations with anyonic statistics. Harnessing these phenomena in electronic devices will lead to a technological breakthrough. The main obstacle to this has been the lack of topological systems that are simultaneously clean, versatile, robust, and highly tunable. We argue that the recent discovery of orbital Chern insulators (OCI) in graphene moiré heterostructures opens an exceptional opportunity to make a leap in our ability to manipulate topological electronic phases.  Recently, moiré superlattices in van der Waals materials emerged as a powerful tool to realize correlated electronic phases. The exciting discovery of intrinsic quantum anomalous Hall effects in graphene moiré systems revealed interaction-driven orbital Chern insulating states at zero magnetic field. Unlike in most known magnets, the magnetism in OCIs arises predominantly from the orbital motion of the electrons rather than their spins, endowing them with unique properties. Remarkably, the moiré heterostructures hosting OCIs could also be gate-tuned to superconducting, correlated insulating, and metallic isospin-ferromagnetic states, which opens unprecedented opportunities for novel devices. These unique features of OCIs set them apart and warrant their thorough investigation.  This proposal aims to establish the fundamental properties of OCIs, focusing on three key questions: (i) What are the phase diagram, isospin order, and thermodynamics of OCIs? (ii) What is the physics of the chiral edge states and domain walls in OCIs? (iii) Can strong interactions in flat moiré bands lead to fractional quantum anomalous Hall effect? To address these questions, we will apply a combination of complementary experimental techniques to probe electronic transport and thermodynamic properties in high-quality graphene moiré devices.

Mots-clés : graphene,moire superlattices,quantum anomalous hall effect,fractional chern insulators,scanning probe microscopy
Projet ERC : GPT-MEDIC (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Innsbruck Medical University — Pays : Autriche — Rôle : PI
Acronyme de l'entité : MUI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1491267.0 €

Background: Artificial intelligence (AI) holds great promise for improving patient care, but challenges related to data irregularity and complexity have hindered its translation into clinical practice. Modelling rich longitudinal electronic health records (EHRs) such as those found in intensive care units (ICUs) remains especially difficult, as they represent a complex interplay between the patient’s health and clinical decisions made in response.  Objectives: We aim to develop a robust AI framework for flexible prediction of any outcome in the ICU and beyond. We will pioneer a class of generative pre-trained models optimised for complex EHR data (Objective 1). Our approach will be rigorously benchmarked across outcomes and hospitals (Objective 2) on a secure, federated infrastructure that ensures data privacy (Objective 3). Methods: Our approach treats EHR data as a stream of clinical events in continuous time and uses generative pre-training to learn both the likely time and the content of the next event. From generated sequences of future events, we will derive explainable predictions for any clinical outcome, including those not encountered before (zero-shot learning). Our approach will be powered by the largest set of harmonised multicentre ICU data to date, covering up to 1 million patients with 33 billion clinical events. We will refine existing methods in federated learning to allow for secure decentralised training of our models at scale.  Innovation & Impact: Our work will introduce a novel paradigm for AI-based clinical risk prediction, setting the stage for a new era of flexible, general-purpose AI in medicine. Our unprecedented multicentre benchmarks will provide an urgently needed baseline for meaningful innovations in the field, while our federated approach will facilitate secure model building across institutions and borders.

Mots-clés : intensive care medicine reproducibility
Projet ERC : YOPOW (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : Université d'Aarhus — Pays : Danemark — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499964.0 €

Political marginalisation, understood as the systematic underrepresentation of social groups in political office, constitutes a major democratic problem. By betraying the ideal of equal voice, it biases political decision making, challenges democratic legitimacy, and ultimately risks creating political alienation and discontent. Yet, we fail to understand the persistence of the problem because extant research has not probed the underlying norms that exist in a society about what political power is, who can hold it, and how it can be exercised. Thus, while we know that members of underrepresented groups do not reach out for power to the same extent as others, we do not have a theory that can explain why the voices missing from politics seem to exclude themselves, while those already represented speak with great fervour.  YOPOW breaks new ground in the study of political marginalisation by locating its roots in societal norms about political power that make a lasting imprint already early in life. The theoretical framework explains how societal norms affect young people through the messages they receive and internalise and the unequal experiences they have with political power. Methodologically pioneering, the project combines machine-learning techniques and in-depth interpretations to detect how societal norms pattern the meaning of political power across societies and social groups. Empirically, YOPOW generates the first-of-its-kind database of messages about political power directed at young people; insights into their beliefs about power expressed in their own words; and novel data on youths’ early experiences with political power in political youth organisations. This project will significantly improve our understanding of one of the most serious and persistent challenges for democracies of today and the future by exposing the societal norms that give rise to biased beliefs and eventually shape who is seen as fit for power among society’s youngest members.

Mots-clés : political marginalisation youth societal norms political power
Projet ERC : AsgOx (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Life Science
Panel : Environmental B iology, Ecology  and E volution
Entité participante : Université d'Aarhus — Pays : Danemark — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1549766.0 €

Many aspects of life's evolutionary history remain poorly understood, such as the emergence of complex eukaryotic life. The recent discovery of Asgard archaea in anoxic sediments has provided fundamental insights into this process due to their phylogenetic proximity to eukaryotes and their eukaryote-like protein repertoire. The cultivation of the first two anaerobic organisms from this group, both belonging to the Lokiarchaeia, provided support for their role in eukaryogenesis, especially with the detection of a eukaryote-like actin cytoskeleton. Despite these advances, there are still many unanswered questions, such as what was the environmental context of this process, especially considering that the Great Oxidation Event (GOE) preceded it. Some hypotheses suggest that mitochondria evolved in anoxic conditions, while others propose a link between higher cell complexity and rising oxygen levels. The Asgard archaea now considered closest to eukaryotes, namely the Heimdallarchaeia class and its subgroups Hodarchaeales and Kariarchaeaceae, are the only ones thought to be facultative aerobes, fueling this debate, though no cultured representatives exist. This proposal aims to deepen our understanding of oxygen-related processes in Asgard archaea, focusing on reactive oxygen species, and to use these organisms to better understand the ecological context of the rise of eukaryotic life. This will involve i) cultivation of the first facultative aerobic Asgard archaea, thought to be the closest to eukaryotes, ii) comparative phylogenetic analysis of Asgard archaea proteins involved in coping with oxygen, iii) biochemical characterization of these proteins using heterologous expression, iv) expression of these proteins in genetically tractable anaerobic archaea. This project will provide unique insights into the ecology and cell biology of our archaeal ancestors, leading to a transformative understanding of the environmental and evolutionary context of eukaryogenesis.

Mots-clés : asgard archaea,cultivation,eukaryogenesis,antioxidant proteins,comparative genomics,phylogenomics
Projet ERC : GLUE (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Texts and Concepts
Entité participante : Université d'Aarhus — Pays : Danemark — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499943.0 €

Social cohesion is a key concept in how we think about the social world. And yet, we have a very poor conceptual understanding of what social cohesion is, how it relates to other social phenomena, and which role it plays in maintaining social order and facilitating cooperation. To date, this challenge has not been taken up by social ontology. Contemporary social ontology primarily focuses on understanding the nature of collective cooperation in which people coalesce around a joint purpose in such a way that we can attribute attitudes and actions to them as a collective rather than as an aggregation of individuals. But social cohesion is not only a function of collective cooperation but also the extent to which a social system enables mutually sustaining cooperation among agents with separate goals. Call this converging cooperation.  It seems then that social cohesion is a key concept in how we think about the social world that lies beyond the scope of social ontology in its current form. Responding to this impasse, the aim of the GLUE project is to secure philosophical clarification of the concept of social cohesion and to account for its role in constituting and maintaining the social world.  The primary research questions are: 1) What is social cohesion, and which distinct social phenomenon might it refer to? 2) What are the semantically explanatory relations between (a) social cohesion and (b) the concepts often used in social ontology and the social sciences to account for collective cooperation? 3) How can a clearer account of social cohesion be used to foster social stability?  Combining descriptive, explanatory, and critical approaches to social ontology, GLUE (a) enriches our typology of social dynamics, (b) expands the scope of social ontology beyond collective cooperation, (c) introduces concepts that can help identify and mitigate forms of social destabilisation, and (d) develops a new and integrative research method for social ontology.

Mots-clés : social cohesion social ontology phenomenology
Projet ERC : ExploreFNP (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth System Science
Entité participante : Université d'Aarhus — Pays : Danemark — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1462491.0 €

Aerosol particles are ubiquitous constituents in the ambient atmosphere. Ultrafine particles (< 10 nm) show adverse effects on our public health, as inhalation leads to an elevated risk of lung- and cardiovascular diseases. Aerosol particles also influence the global climate, by scattering sunlight away from Earth's surface and acting as seeds for cloud droplet formation. Combined, these effects lead to an overall cooling of the Earth, directly counteracting the warming effect of greenhouse gases. According to the IPCC, aerosol particles pose the largest uncertainty in global climate forecast. This uncertainty is caused by the lack of understanding of the early growth behaviour of small (< 3 nm) particles. The largest source of aerosol particles (50-90%) is from nucleation of vapours in the air leading to a burst of freshly nucleated particles (FNPs) of 1-2 nm in size. However, even the basic fundamental properties of these FNPs remain unknown and cannot be studied using currently available experimental techniques. I propose a unique approach to target the properties of FNPs by applying a versatile suite of computational methods, ranging from quantum chemical calculations to application of conceptually new machine learning models. The scientific objectives are: 1) To determine the chemical composition and stability of FNPs. 2) To understand how FNPs evolve over time via exchange of vapours with the environment. 3) To investigate how FNPs transform as a consequence of chemical reactions occurring at the surface or inside the particles. The research will provide unprecedented insight into the molecular level properties of FNPs. This project will directly supply input parameters (chemical composition, thermodynamics and kinetics) for atmospheric models, which are crucial in order to constrain the large uncertainty in climate predictions caused by small aerosol particles.

Mots-clés : aerosols,nucleation,clusters,quantum chemistry
Projet ERC : ClimAdaptLife (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Life Science
Panel : Environmental B iology, Ecology  and E volution
Entité participante : Université d'Aarhus — Pays : Danemark — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1497883.0 €

Increasing climatic fluctuations mean that animals need to evolve increased heat resilience whilst simultaneously coping with cold periods throughout their lives. Large animals are particularly susceptible to temperature fluctuations as their large size when mature exposes them to heat stress, and their small size when juvenile exposes them to cold stress. I will test two hypotheses that are central to understanding how large species evolve to cope with temperature fluctuations: 1) Genomic adaptations that increase heat resilience compromise cold resilience. 2) Genes that increase thermal resilience during early life have opposing effects on thermal resilience later in life. Testing these hypotheses requires identifying the genomic basis of thermal resilience. This has remained elusive in large animals because of difficulties in 1) rearing high numbers of large animals, 2) exposing them to natural temperature fluctuations and 3) obtaining measurements of fitness related traits such as growth of juveniles and reproduction of adults. I will solve these issues by studying the largest bird on Earth, the ostrich, monitored in large numbers under natural temperature conditions. This will allow me to take an interdisciplinary approach combining body temperature measurements and cutting-edge genotyping of 2000 juveniles and adults. A close partnership with a South African research station will enable me to utilise data of growth and reproduction of 11700 juveniles and 1800 adults exposed to the natural climate over 25 years. To test the importance of identified genes for local climatic adaptation I will genotype natural populations of ostriches across climatic gradients. My work will resolve how genes conferring resilience to heat influence the ability to cope with cold, and if the same genes are important across different life-stages. This will shape our understanding of thermal evolution in endothermic animals at a critical time.

Mots-clés : life stages,temperature fluctuations,heat tolerance,cold tolerance,thermal tolerance,genetic diversity,thermal adaptation,thermoregulation,large animals,thermal fertility limits,