Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : Human3D (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : École polytechnique fédérale de Zurich — Pays : Suisse — Rôle : PI
Acronyme de l'entité : ETH Zurich
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

The world is entering the age of autonomy, where systems like self-driving cars and assistive robots face the challenge of perceiving and understanding human behavior in complex, dynamic environments through sensory inputs like images. While human motion and behavior have been studied extensively using visual data, existing methods often struggle in open-world settings due to their inability to generalize beyond domain-specific training data. The goal of this project is to learn generalizable visual representations from large-scale training data and create a foundational model that supports various human analysis and modeling tasks, grounded in 3D environments, in an open-world setting. Our key objectives include (1) Devising new human-centric representations and algorithms for accurate 3D human motion and appearance reconstruction from in-the-wild videos. (2) Developing advanced generative models synthesizes large-scale, realistic virtual humans, emphasizing controllability and efficiency. (3) Designing physics-guided methods for long-duration human-scene interaction capture using embodied devices. (4) Studying efficient learning algorithms and unified model architectures for 3D human foundation models that enable efficient fine-tuning and generalization across various human-centric computer vision tasks. We aim to establish the computational and algorithmic groundwork for human-centric visual foundation models, with advancement in generative modeling, human-centric representation learning, and visual data simulation. Our work will facilitate the development of autonomous AI agents for various sectors, including autonomous driving, extended reality, education, and manufacturing. Ultimately, this project is committed to advancing human-centric AI technologies, fostering more robust and effective human-AI interaction, and paving the way for practical applications that significantly improve everyday life and industrial processes.

Mots-clés : digital humans,human motion capture,egocentric computer vision,3d foundation models,generative models
Projet ERC : BacterialBlueprint (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Life Science
Panel : Cell Biology, Development, Stem Cells and R egeneration
Entité participante : KU Leuven — Pays : Belgique — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Modern metagenomics has opened our eyes to the immense bacterial diversity that exists both among and within us. Despite this diversity, all bacteria share the basic challenge of organizing the various processes that ensure their faithful replication. All bacterial cells need to metabolize nutrients, generate building blocks, maintain their shape and size, replicate and segregate their chromosomes, synthesize cell walls and membranes, and divide to give rise to daughter cells. At present, we do not understand how bacteria integrate all these processes in their small cellular compartments. What makes this question even more intriguing is that bacteria represent simple forms of proliferating cells, without additional layers of internal organization (e.g., membrane-enclosed organelles) or cell cycle regulation (e.g., cyclins and cyclin-dependent kinases) seen in eukaryotic cells. My goal is to address this gap by uncovering the internal architecture of bacterial replication and identifying the molecular mechanisms that underlie it. I will use a high-throughput single-cell phenomics approach that I developed and that provides high-content, quantitative cell biological information. By applying this approach across different levels of bacterial diversity (both within and across species, beyond the small number of currently existing model species), I aim to identify general and species-specific principles for the subcellular organization of replication in bacteria. This analysis will also enable the identification of key factors involved in establishing these governing principles, which will be functionally characterized further to provide a unique overview of the molecular mechanisms that determine the spatial organization of bacterial replication. If successful, this project will transform our understanding of bacterial cell biology by expanding it beyond current textbook standards and providing us with the blueprints and design principles of bacterial cells.

Mots-clés : bacterial cell biology,intracellular organization,cell cycle,cell morphogenesis,high throughput single cell phenotyping,bacterial systems biology,quantitative fluorescence microscopy
Projet ERC : LINING (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : École polytechnique de Turin — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499069.0 €

The lack of fundamental knowledge of the interaction between an acoustic wave and a turbulent boundary layer grazing an acoustically treated surface, such as an acoustic liner, is the cause of unexpected and unphysical results found when performing the acoustic characterization of the sound absorbing surface with inverse eduction methods. This is because, in this field, acoustic and aerodynamic have never been fully coupled. To fill this knowledge gap, the acoustic and hydrodynamic velocities near an acoustically treated surface must be measured. Since it cannot be done only with state-of-the-art experiments, because of hardware and field-of-view limitations, I propose to complement experiments with scale-resolved high-fidelity numerical simulations based on the lattice-Boltzmann very-large-eddy simulation method. Numerical results will be used to explain the physics of the acoustic-flow interaction. Advanced data analysis methodologies will be developed and applied to separate the acoustic-induced velocity near the wall from the hydrodynamic one. At the same time, the numerical database will be used to compare inverse methods, employed to acoustically characterize the sound absorbing surfaces, in order to explain the physical reasons behind the unexpected results, and propose physics-based corrections. Furthermore, by describing the flow-acoustic interaction, it will be possible to model and predict the drag increase caused by the coupling between the acoustic-induced velocity and the free-stream one. My description of the flow-acoustic interaction will solve the scientific debate about the unexpected results and pave the way towards future broadband low-noise low-drag acoustic meta-surfaces to increase propulsion efficiency and reduce noise of future, more sustainable, aircraft engines.

Mots-clés : aeroacoustics,impedance,flow acoustic interaction
Projet ERC : FIND (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Human Mobility, Environment, and Space
Entité participante : École supérieure Sainte-Anne de Pise — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1124375.0 €

The FIND project will develop an innovative framework to assess the feasibility and social desirability of limiting global warming through the diffusion of negative emission technologies. Current global climate action is deeply insufficient to deliver the objectives of the Paris Agreement and containing global warming to 1.5 °C will likely require the deployment of carbon dioxide removals. However, the technologies to sequestrate and store carbon from the atmosphere are currently immature, risky, and highly questioned. Understanding the effective diffusion potential of carbon removal methods and their socioeconomic and environmental impacts is pivotal to design future climate action. The FIND project aims to ensure that negative emission technologies act as an enabler, not a barrier, of long-run sustainable development. It brings together different disciplines – namely, climate science, economics, innovation studies, climate finance, integrated assessment, and agent-based modelling - in a coherent and synergic whole. FIND is designed to provide breakthrough evidence about two crucial and under-investigated aspects of carbon removal solutions: (i) how their techno-economic paradigms evolve and relate to the broader technological landscape, and (ii) how immature and uncertain technologies can be financed to provide social value rather than speculative interest. By combing innovation, finance, and political economy into a quantitative theory of carbon removal operationalization unfolding at global scale, FIND will expand the state-of-the-art in climate-energy-economy modelling and reassess decarbonization pathways. The project will evaluate climate and non-climate policies to create robust, no-regret policy portfolios supporting a rapid and sustainable path to a net-zero society. FIND will be of high relevance for public policy and civil society, especially considering Europe’s commitment to reach carbon neutrality by 2050 while spurring green and inclusive growth.

Mots-clés : negative emission technologies sustainable development innovation technological diffusion climate finance integrated assessment modelling
Projet ERC : WINGS (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe Sciences
Entité participante : École normale supérieure de Pise — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1199519.0 €

Massive black holes (BHs) reside at the center of galaxies and release an extraordinary amount of energy during their accretion phase. Such energy drives galaxy-scale gas outflows that can significantly influence the evolution of the host galaxy. Despite the remarkable theoretical and observational progress in quantifying the energetics and dynamics of such outflows, establishing their role during the formation and evolution of galaxies remains a long-standing problem in modern astrophysics. WINGS is designed to finally settle whether outflows driven by BHs are able to shape galaxies and their environment or not. To achieve this goal, we will build up a novel observational and theoretical framework that investigates the role of outflows in the early Universe, when BHs and galaxies are growing at the highest rates and outflows are expected to be more common and powerful, leaving a prominent mark on galaxy evolution. Thanks to the PI, WINGS will have access to several key observing programs carried out with ALMA and VLT as well as the guaranteed-time observer (GTO) program (~900 hours) with the NIRSpec spectrograph on board the soon-to-launch JWST. By exploiting both observations of local outflows and zoom-in simulations, we will develop a 3D kinematic outflow model that will be an asset in (i) interpreting existing high-redshift data and upcoming JWST observations, (ii) characterizing for the first time outflow properties across cosmic time (up to z~9), and (iii) identifying feedback signatures in the interstellar and circumgalactic medium properties. The combination of JWST, ALMA, and VLT observations will enable WINGS to assemble the largest high-spatial resolution survey of multi-phase outflows. The support from the ERC will be crucial to assemble a unique team mixing the observation-oriented and theory-oriented approach, as a joint effort of observations and simulations is the key to make a major breakthrough in understanding outflows driven by BHs.

Mots-clés : black holes,outflows,high redshift universe
Projet ERC : LOUARN (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth System Science
Entité participante : Institut météorologique finlandais — Pays : Finlande — Rôle : PI
Acronyme de l'entité : FMI
Type d'entité : Recherche
Financement ERC de l'entité : 2459229.0 €

This project will address a major unsolved question in geosciences: how strongly is our planet's atmosphere subjected to forcing from space? The mesosphere–lower-thermosphere–ionosphere (MLTI) is the boundary of the Earth environment, and as such it undergoes forcing from above, in particular at high latitudes. The forcing includes charged particle precipitation from near-Earth space, associated with electric currents producing Joule heating in the upper atmosphere. It also involves trace gas production in the mesosphere which catalytically destroy ozone – including down to the stratosphere when long-lived species descend to lower altitudes – as well as neutral wind changes and atmospheric wave generation. However, these effects in terms of MLTI energetics, chemistry, and dynamics are largely unquantified, leading to unknown uncertainties in lower atmosphere and climate model results. The LOUARN project will bridge this gap by adopting a multidisciplinary approach, combining knowledge and methods from space physics and atmosphere sciences. With LOUARN, I aim at unravelling the MLTI forcing from space by using cutting-edge ground-based instruments measuring both the ionised medium and the neutral gas properties, together with numerical simulations of the upper atmosphere and novel citizen science methods. My objectives are to (1) quantify the energy deposited into the MLTI during geomagnetic disturbances, (2) provide accurate upper-boundary conditions on the ozone balance for atmosphere and climate models, and (3) characterise the wind and atmospheric wave response to this driving from space. My expertise in ionospheric physics and its driving from near-Earth space processes, along with my leadership in auroral citizen science, puts me in an ideal position to address this ambitious challenge. I aim at a breakthrough in atmosphere–ionosphere–space couplings with impacts on atmosphere and climate sciences and on solar–terrestrial physics.

Mots-clés : mesosphere lower thermosphere ionosphere,auroral physics,particle precipitation,ionospheric currents,subauroral processes,citizen science,mesospheric and stratospheric ozone,upper atmospheric waves,substorm
Projet ERC : ForExD (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth System Science
Entité participante : Université de Leipzig — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 996734.42 €

Forests play a crucial role not only in providing breathable oxygen, supporting biodiversity and regulating water and energy exchanges, but also in mitigating climate change. Currently, forests absorb 25% of human CO2 emissions, but their sequestration potential may be negated by more frequent and intense weather extremes and disturbances in the future. Mega-droughts, massive wildfires and widespread tree mortality reported in the last decade might be early warnings of the upcoming threats to forests under climate change. Yet, process-understanding about the interactions between climate, forests and key disturbances is limited. Consequently, impacts of compound extremes and disturbances on forest CO2 sequestration potential are not yet realistically simulated in future climate projections by Earth System models. To overcome this knowledge gap, ForExD proposes a cross-disciplinary conceptual framework placing disturbances as intrinsic components of coupled ecoclimatic variability. We will apply novel high spatiotemporal resolution remote-sensing data to reconstruct disturbance histories and explore emerging analytical tools to gain understanding about the interactions between climate, forests and disturbances. We will then develop mechanistic models of forest-disturbance interactions, which will allow simulating, for the first time, compound extremes and disturbances (fires, droughts, insects, storms) and impacts in a state-of-the-art land-surface model. The project will build on the PI's expertise on ecological monitoring and modelling and strong cross-disciplinary background on ecoclimatology and biogeochemistry to develop a systemic understanding of interactions between climate, forests and disturbances. ForExD will provide fundamental knowledge about forest dynamics and feedbacks between climate and the carbon-cycle. In doing so, ForExD will make a vital contribution to resolve long-standing uncertainties about forests' climate change mitigation potential.

Mots-clés : remote sensing,land surface modelling
Projet ERC : IProbability (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : Université de Leipzig — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1083750.0 €

This project is devoted to integrable probability. The key feature of the field is the prominent role of methods and ideas from other parts of mathematics (such as representation theory, combinatorics, integrable systems, and others) which are applied to stochastic models. This philosophy often leads to very precise limit theorems which seem to be inaccessible by more standard probabilistic techniques.  The proposed research is a study of a variety of probabilistic models. Specific examples include the single- and multi-species asymmetric simple exclusion process, a six vertex model, random walks on Hecke, Temperley-Lieb, and Brauer algebras, random tilings models, and random representations. The suggested methodology consists of a range of probabilistic, algebraic, analytic, and combinatorial techniques. The project involves two circles of questions. The first one focuses on random walks on algebras and their applications to interacting particle systems. The specific objectives include studying the Kardar-Parisi-Zhang type fluctuations for the multi-species asymmetric simple exclusion process, computing limit shapes and fluctuations around them for a general six vertex model, introducing and studying integrable three-dimensional analogues of a six vertex model, and developing a general theory of random walks on algebras.  The second one focuses on asymptotic representation theory. This area deals with the probabilistic description of representations of “big” groups. Such questions turn out to be related to a plethora of other probabilistic models, in particular, to models of statistical mechanics. The goals of this part include bringing this interplay to a new level, developing asymptotic representation theory of quantum groups, and studying random tilings in random environment. The unifying idea behind these questions is a systematic use of precise relations for the study of asymptotic behavior of stochastic models which are out of reach of any other techniques.

Mots-clés : asymptotic representation theory,exclusion processes,random walks on algebras,six vertex model,random tilings
Projet ERC : CILCat (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : Université Friedrich-Schiller d'Iéna — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1498590.0 €

Ammonia is one of the most important chemicals in the world. Electrocatalytic reduction of nitrogen (NRR) at ambient conditions is a sustainable alternative for its production to the established energy consuming Haber-Bosch process, relying on hydrogen from fossil sources. The triple bond in dinitrogen is one of the most stable covalent chemical bonds. Conversely, the dissociation of dinitrogen and its chemical conversion is highly demanding. NRR is a carbon-neutral and decentral process that can be carried out wherever renewable electricity, water, and air are available. However, current research on NRR and other electrocatalytic reactions has reached an impasse as improvements based on catalyst design are getting more and more incremental. At this tipping point, CILCat tackles a foreseeable stagnation by constituting a disruptive principle that holds holistic perspectives for the activation of small molecules. The novel concept will go beyond established principles of isolated catalytically active sites. By confining ionic liquid (IL) electrolytes into charged porous carbon materials, an interface will be created, that as a whole serves as catalytic surface. CILCat will contribute to a fundamental understanding of the physicochemical principles of sorption into ILs upon confinement in pores. Targeted catalyst development will follow and the possibility of using the principle for catalytic activation of nitrogen and other molecules will be explored. This innovative approach will then be combined with advanced electrode design. CILCat aims for more than a step towards a future carbon-free nitrogen economy. It is a pioneering attempt to heterogenize homogenous catalysts by rather converting the energy principles of small molecule activation than chemical structures from solution to surfaces. The methodology is transferable to other obstacles in the field of catalysis and the project will lead to a more objective general understanding of reactivity in confined spaces.

Mots-clés : electrocatalysis porous carbon materials nanoconfinement ionic liquids activation of small molecules
Projet ERC : CollectiveDecisions (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Université de Constance — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1498787.0 €

It is challenging for isolated animals to reliably extract and integrate behaviorally relevant information from the natural environment. Due to the limited sensory capacity of individuals, many animal species therefore share and evaluate cues collectively, allowing them to solve complex decision-making tasks as a group. The behavioral algorithms and neural mechanisms that give rise to these cognitive abilities remain poorly understood. In many fish species, these behaviors are largely vision-based, providing the opportunity to decipher the underlying general computational principles under well-controlled experimental conditions in the lab. At the same time, it is becoming possible to employ powerful neuroscientific techniques, enabling new detailed analyses of the neural circuitry that orchestrates behavior. I propose to establish the juvenile zebrafish as a model system that is optimally suited for the study of collective decision-making. At this intermediate developmental stage, zebrafish offer an excellent compromise between cognitive ability and experimental accessibility. They can temporally and spatially integrate information, they start to socially interact, and one can characterize and manipulate brain activity in intact behaving animals. Using closed-loop virtual reality experiments, I will initially dissect the algorithmic rules by which juvenile zebrafish make decisions when swimming in heterogeneously biased groups. I will then characterize brain activity related to this behavior, in freely swimming fish, and in restrained preparations. Finally, to causally link neural circuit function and group decision-making performance, I will carry out targeted laser ablation and optogenetic activation experiments. Thus, my proposed research in juvenile zebrafish will, for the first time, provide key insights into the behavioral algorithms and neural mechanisms of how individual animals and animal collectives acquire sensory information and make complex decisions.

Mots-clés : decision making collective behavior behavioral modeling virtual reality zebrafish two photon microscopy