Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : Gen3D (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Université technique de Munich — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2750000.0 €

In recent years, we have seen a revolution of learning methods that generate highly-realistic images, such as generative adversarial neural networks, autoregressive methods, or diffusion models (e.g., DALL-E, Stable Diffusion, Runway, etc). Unfortunately, the vast majority of these methods are tailored towards the 2D image domain, while their respective 3D counterparts – 3D models that fuel computer graphics applications, and enable visually immersive experiences – remain in their infancy. In this proposal, we tackle the challenge of automatic generation of 3D content for virtual worlds. Such 3D generated content enables versatility, with flexible rendering from arbitrary viewpoints that match the visual fidelity of the real world. We focus on 3D content creation for visually immersive experiences for a much wider audience in myriad applications, such as video games, movies, AR/VR scenarios, CAD modeling, architectural & industrial design, and medical applications. We believe that the key towards automated, high-fidelity content creation lies in developing new machine learning techniques to transform 3D content generation.  (A) We will develop 3D Generative Models that output 3D polygon meshes, along with their surface textures and material properties, highlighting generation of 3D content that can be directly consumed by modern graphics pipelines.  (B) To train our 3D generative models to reflect the complexity and diversity of real data, we will devise methods for Supervision from Images and Videos. The key challenge here is that such collections of images and videos are by nature incomplete projections of the underlying 3D world, thus requiring learning paradigms that generalize across partial instances.  (C) We will research techniques that provide Control and Editability through Conditional Generation. In particular, we will focus on conditional input from both novice (e.g., text-based editing) and expert (e.g., based on existing authoring tools) users alike.
Projet ERC : PREDDYCT (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth System Science
Entité participante : Centre Européen pour les Prévisions Météorologiques à Moyen Terme — Pays : Royaume-Uni — Rôle : Partner
Acronyme de l'entité : CEPMMT
Type d'entité : Recherche
Financement ERC de l'entité : 305105.0 €

Climate predictions are our most valuable tools to support socio-economic decision-making from regional to local scales and develop successful adaptation strategies. Their level of accuracy is determined by our understanding of the climate system and our capacity to simulate it correctly. While current prediction models exhibit high skill in predicting sea surface temperature in key regions – like the North Atlantic or the Tropical Pacific – from months to years in advance, their ability to predict the atmospheric circulation and through it the continental climate is undermined by structural model problems. These problems point to a misrepresentation of key processes and interactions. PREDDYCT focuses on the North Atlantic, the region where the structural problems manifest more clearly. Its aim is to bring a new fundamental understanding of the physical mechanisms that need to be realistically simulated to provide climate predictability to its neighbouring continents. The main working hypothesis is that mesoscale eddies – whose contribution is unresolved in current prediction models – are the key element. This is supported by the fact that in the North Atlantic region, resolving the effect of mesoscale ocean eddies and the atmospheric eddy feedback onto the midlatitude jet has been shown to critically improve the realism of air-sea interactions and their influence on the large-scale atmospheric circulation. In light of this, PREDDYCT will combine a new generation of predictions at ground-breaking resolutions, an innovative tuning framework to enhance their accuracy, and extensive process-oriented analyses to, for the first time, resolve and understand the contribution of mesoscale eddies to North Atlantic climate predictability. PREDDYCT’s new conceptual and methodological insights will pave the way for further forecasting advances at the global scale and contribute to achieve a long-awaited breakthrough in the realism and trustworthiness of climate predictions.

Mots-clés : climate prediction,high resolution climate modelling,air sea interactions and feedbacks,ocean and atmospheric dynamics
Projet ERC : CURE (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : École Nationale Supérieure des Mines de Paris — Pays : France — Rôle : Partner
Acronyme de l'entité : Mines Paris - PSL
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 450608.0 €

Developing new capabilities to predict the risk of intracranial aneurysm rupture and to improve treatment outcomes in the follow-up of endovascular repair is of tremendous medical and societal interest, both to support decision-making and assessment of treatment options by medical doctors, and to improve the life quality and expectancy of patients.  The proposal aims at identifying and characterizing novel flow-deviator stent devices through a high-fidelity computational framework that combines state-of-the-art numerical methods for fluid-structure interaction modeling (to accurately describe the mechanical exchanges between the blood flow, the surrounding vessel tissue, and the flow-deviator) and deep reinforcement learning algorithms (to identify and to invent a new stent concepts enabling patient-specific treatment via accurate adjustment of the functional parameters in the implanted state). This has never been done before in this context and should thus open both new theoretical and numerical opportunities.  CURE takes the vital steps of bringing novel computational and optimization frameworks to the next level capable of studying the selected flow diverter treatment in order to reduce the risk of hemorrhage in cerebral aneurysms, of supporting the decisions of treatment options by medical doctors and finally of providing guidance in the development of new implant design. Such unique capabilities can save millions of lives worldwide, improve the life quality of patients, eliminate lifelong side-effects due to sub-optimal treatment planning and delivery; and reduce the tremendous societal and economic burden linked to poor patient outcome.  The proposed work has potential to reshape the future of intracranial aneurysm risk management. It is highly multidisciplinary, and the methods proposed and developed as a part of this research can be quickly adapted to a wide range of engineering and bio-medical applications.

Mots-clés : unruptured intracranial aneurysms fluid structure interaction computational fluid dynamics machine learning deep reinforcement learning endovascular treatment patient specific stent design
Projet ERC : FunMicroRetina (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and Communication Engineering
Entité participante : Université libre d'Amsterdam — Pays : Pays-Bas — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999998.0 €

Subcellular, functional imaging of neurons in living humans could revolutionize biomedical research and prove valuable in the clinic. With optical techniques, subcellular resolution is achievable and although the eye provides optical access to the retinal neurons, the size of the eye’s pupil currently prevents subcellular resolution in all optical imaging techniques of the living retina. In this project, I will develop a novel tomographic microscopy technique and use it to demonstrate at least a 4-times better resolution for retinal tomography compared to the state-of-the-art. For the first time, a resolution below 1 μm will be achieved for imaging of the living human retina. For this, I will use innovative structured illumination and reconstruction techniques that project coherent light through the sclera (transscleral) or the skull (transcranial), allowing for unprecedented resolution in 3-D retinal tomography. My method will use interferometric detection with noiseless amplification and exploit recent computational methods to reconstruct and correct the data. In addition to morphology, my technique will also assess the functional state and activity of neurons after optical stimulation of the photoreceptors. For this, I will harness changes in phase and speckles of the image data caused by physiological processes. This imaging will be non-invasive and without contrast agents. With this technology, I want to enable researchers and clinicians to study the detailed structure and function of individual neurons and their interactions in the living human eye with microscopic 3-D tomography. This revolutionary approach may provide new insights into human physiology and aid in the diagnosis and treatment of ophthalmic and neurological conditions.  With a strong background in high-performance computational imaging methods, their application to retinal imaging, and experience in both industry and academia, I am uniquely qualified for this groundbreaking research.

Mots-clés : optical imaging,retina imaging,optical coherence tomography,microscopy,structured illumination,neurology,ophthalmology
Projet ERC : WasteMatters (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Human Mobility, Environment, and Space
Entité participante : Université de Tampere — Pays : Finlande — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1974800.0 €

The future world imagined by the circular economic model is a world without waste, provided that materials and energy circulate efficiently enough through society. The project WasteMatters disrupts the waste-as-resource paradigm prevalent in the circular economy related politics, business, and scholarship alike, and examines our contemporary trashscapes beyond the idea(l) of the eternal redemption of waste. Its novel approach is to analyse the leaky realities of waste as integral to the human condition and as constitutive of society. The project explores the implications of waste throughout society and what waste does for/to us; what kind of relations, agencies, and spatiotemporal scales it assumes, prompts, enacts, and sustains; to what kind of futures society commits itself with it; and what humans become with waste. The project develops a new methodology, more-than-human ethnography, to be able to attend to the vibrant nature and active role of waste in how we live together. The research will be carried out through four sub-studies across various sites (in Sweden and Finland), which function as important nodal points in the management and circulation of wastes: households, businesses, biogas plants, waste incinerators, and a nuclear waste repository and, to attend to the multiplicity of waste, the project will focus on four kinds of waste: food waste, plastic waste, waste incineration ash, and nuclear waste. Through the research design, the project will generate ground-breaking insight into waste as both constitutive of society and as something that disturbs it. The empirical, methodological, and conceptual insights will be combined to enable a leap in theory building to develop a new relational theory of waste that pays attention to the multiple spatiotemporal scales of society and human actions, ultimately leading to a paradigm change from the circular economy to waste matter society.

Mots-clés : broken world thinking circular economy ethnography new materialism scale social theory waste
Projet ERC : electroANME (Horizon Europe, appel 2025, Consolidator grants)
Entité participante : Université Radboud de Nimègue — Pays : Pays-Bas — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2000000.0 €

Anaerobic methanotrophic (ANME) archaea are ecologically critical microorganisms that remove methane from natural environments, thereby reducing emissions of this potent greenhouse gas. However, their physiology and ecology remain vastly unexplored due to the lack of axenic (single species) cultures of ANME archaea, their slow doubling times (in the order of weeks or months) and their complex nutritional requirements and interactions with other organisms. Recently, we achieved a breakthrough in ANME archaea cultivation, developing bioelectrochemical systems with highly enriched (>95% pure) and rapidly growing (doubling time ~4.5 days) cultures of Ca. Methanoperedens, a representative freshwater ANME archaeon. Now, in electroANME, we propose to leverage our expertise in growing and characterizing ANME archaea and our unique access to these near-axenic cultures to address urgent scientific questions related to microbial methane removal. In Aim 1, we will elucidate the molecular mechanisms involved in methane oxidation, particularly by elucidating how ANME archaea perform extracellular electron transfer to electron acceptors. In Aim 2, we will identify bacterial partners of ANME archaea and delineate the mechanisms involved in direct interspecies electron transfer between ANME archaea and bacteria. In Aim 3, we will explore the metabolic diversity of ANME archaea, including in the absence of external electron acceptors, and probe the environmental relevance of these metabolic processes. The findings from this project will not only provide novel insights into the physiology and ecology of ANME archaea but also have the potential to improve climate models and inform future biotechnological applications exploring the capacity of these microbes to oxidize methane, thereby helping to address the pressing societal problem of climate change.

Mots-clés : microbial physiology,archaea,microbial interactions,climate change,methane,methanotrophs
Projet ERC : HOMCRI (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Human Mobility, Environment, and Space
Entité participante : Université de Tampere — Pays : Finlande — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1923180.0 €

This project elaborates ways of making home among those dwelling in societies facing protracted crises. It traverses through various landscapes to look at ways in which people make home in spaces that are familiar, yet repelling, incapacitating and altogether negating in nature. Such landscapes, notably in Lebanon, the West Bank and Gaza, reflect various forms of crises engendered around economic collapse, infrastructural shortage, prolonged conflict-situation and/or continuation of war by other means. These crises force us to pose a key question on what it means to stay, and make a home, in spaces that constantly expose life to disruptions, incapacitations, and material negations. How does one dwell in crisis? The project responds to this research challenge via ground-breaking research that goes beyond the state-of-art on three fronts. Firstly, it generates vast empirical knowledge on what it takes to dwell in crisis and conflict areas, and with the political conditions they establish, by focusing on spaces that violently separate, distance, and amputate people from their familiar everyday spaces through constant affective disruptions, material deprivations, and conditions of incapacitation. Secondly, it does so by developing negativity as a novel methodological tool for approaching dwelling as a tension between ‘home-making’ and ‘spaces of exposure’. Thirdly, it offers a novel conceptual elaboration of negativity as a worldly condition, which challenges the paradigmatic notions of materiality, affect and dwelling in current posthuman thought. Designed for high-gain outputs, the project takes a high risk in offering ground-breaking research that aims to fundamentally rethink the negative foundations of human-world relationship by focusing on ways in which negative material and affective bindings align with incapacitating political conditions in prolonged crisis and conflict situations.

Mots-clés : dwelling,political ontology,affect,materiality,middle east
Projet ERC : NEOGRAFT (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Amsterdam University Medical Centers — Pays : Pays-Bas — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 1934500.0 €

Global mortality from chronic kidney disease (CKD) is estimated at more than 1.2 million annually. CKD is caused by the irreversible loss of nephrons, the structural and functional units of the kidney. Lineage tracing revealed that all renal cells are derived from a SIX2+ embryonic progenitor population originating at the cap mesenchyme (CM). SIX2 is essential for cells’ self-renewal, and its cessation signals the initiation of nephron differentiation. However, SIX2+ progenitor cells are exhausted in the mature kidney. My team has recently discovered a novel unique source of kidney stem/progenitor cells (nKSPCs) derived from the urine of neonates born before the completion of nephrogenesis. nKSPCs express SIX2 as well as various other CM progenitor markers, and they show a high capacity to differentiate into functional kidney epithelial cells. Additionally, our data show that nKSPCs have an immunomodulatory capacity in vitro. Using normothermic machine perfusion (NMP) of human kidneys, we have discovered that nKSPCs are able to reduce inflammatory responses and activate regenerative processes. Building on these findings, I aim to establish nKSPCs as a potent novel cell type for the development of kidney-targeted cell therapy for preconditioning kidney allografts prior to transplantation. To this end, I will elucidate the immunomodulatory profile of nKSPCs and demonstrate their potential for engraftment and regeneration at single cell level using advanced methodologies for the first time in this field. Moreover, I will use the ground-breaking pre-clinical platform of whole organ culture system to trace single nKSPCs in kidney tissue over a longer period of time after injection. nKSPCs are an unexplored progenitor cell type, which might revolutionize the field of kidney diseases. My long-term goal is to establish clinical-grade nKSPCs for the development of kidney-targeted cell therapy with the capacity to directly immunomodulate and regenerate kidney tissue.

Mots-clés : chronic kidney disease,kidney transplantation,kidney stem progenitor cells,kidney graft,kidney regeneration,immunomodulation,normothermic machine perfusion,whole organ culture system,single nucleous rnaseq
Projet ERC : INFER (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Materials Engineering
Entité participante : Université de Linköping — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999980.0 €

The development of advanced brain-computer interfaces, wearable and implantable bioelectronic devices, prosthetics, and soft robotics requires the ability to process signals in a highly individualized and localized manner. To achieve this, new materials and devices must be developed that can sense their surroundings, process information locally, and translate it into a format our body can interpret. Currently, (bio-)electronic devices rely on remote and energy-intensive cloud processing, but electronic devices that mimic the design of the human brain offer a solution. However, silicon-based devices have limitations such as rigidity, poor biocompatibility, and operating principles that differ from the ion signal modulation of biology. Organic mixed ionic-electronic conductors (OMIECs) have emerged as a promising option in the field of bioelectronics, as they are solution processable, potentially biocompatible, and can transport both electronic and ionic signals. The goal of INFER is to create next-generation intelligent bioelectronic devices using in-operando electropolymerization of OMIEC monomers. The proposed research activities aim to 1) understand how the molecular properties of OMIEC monomers impact their in-operando electropolymerization and the learning capabilities of the resulting biomimetic devices, 2) achieve biorealistic speeds, memory functionalities, and energy efficiencies without the use of auxiliary devices, and 3) prototype devices that can locally sense, process, and actuate/stimulate. The long-term goal is to create a brain-inspired intelligent bioelectronic platform that brings a new paradigm for in-sensor computing at the interface with biology.

Mots-clés : organic mixed ion electron conductors,conjugated polymers,organic electronics,organic bioelectronics,organic electrochemical transistors
Projet ERC : EMMA (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and Analytical Chemical Sciences
Entité participante : Université technique du Danemark — Pays : Danemark — Rôle : PI
Acronyme de l'entité : DTU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1995554.0 €

The last years have seen unprecedented breakthroughs in protein structural biology, with the resolution revolution in cryo-electron microscopy and the release of AlphaFold. The combination of advanced experimental structural biology, machine learning algorithms and molecular simulations has put the fully quantitative description of how the structure and interactions of folded proteins are defined by their amino acid sequence within close reach. This leaves us with a final frontier in protein science, namely to achieve a similar level of understanding for intrinsically disordered proteins (IDPs). The energy landscapes of IDPs often comprise a multitude of nearly isoenergetic states, that include assembled forms, such as amyloid fibrils and liquid condensate droplets. Much effort has been spent in order to achieve an understanding of the molecular grammar of IDP assembly and condensation, i.e. how amino acid sequence defines both kinetics and thermodynamics of these processes. Current state of the art is to evaluate a few dozens of sequence perturbations quantitatively in vitro. In EMMA, I propose to develop a fundamentally new approach that will ultimately allow to improve on current methods by more than 8 orders of magnitude. This ground-breaking improvement will be achieved by exploiting the power of mRNA display, in which the biophysical behavior of each individual sequence within large libraries of protein-mRNA constructs can be evaluated in a “one pot” reaction. We will combine quantitative screening of the energetics of liquid condensate droplet formation of up to 10^10 sequences by mRNA display with a multiparametric biophysical toolbox that allows the key thermodynamic and kinetic parameters of binding and assembly to be evaluated for thousands of selected sequence variants. EMMA will transform our ability to probe the mechanisms and interrelationships of the interactions and assembly processes that define IDP function and disease-related malfunction.

Mots-clés : amyloid,liquid liquid phase separation,mrna display,energy landscape,sequence space exploration