Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : DEEPCONTROL (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : Université de Poitiers — Pays : France — Rôle : Partner
Acronyme de l'entité : Université Poitiers
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 60000.0 €

Over the past decades, aviation has become an essential component of today’s globalized world: before the current pandemic of coronavirus disease 2019 (COVID-19), over 100,000 flights took off everyday worldwide, and a number of studies indicate that after the pandemic its relevance in the transportation mix will be similar to that before COVID-19. Aviation alone is responsible for 12% of the carbon dioxide emissions from the whole transportation sector, and for 3% of the total CO2 emissions in the world. Due to the major environmental and economical impacts associated to aviation, there is a pressing need for improving the aerodynamic performance of airplane wings to reduce fuel consumption and emissions. This implies reducing the force parallel to the incoming flow, i.e. the drag, and one of the strategies to achieve such a reduction is to perform flow control. DEEPCONTROL aims at using high-fidelity simulations and deep reinforcement learning to develop a framework for real-time prediction and control of the flow around wing sections and three-dimensional wings based only on sparse measurements. We will first perform high-order spectral-element simulations of wing sections and three-dimensional wings at high Reynolds numbers. Using sparse measurements at the wall, we will reconstruct the velocity fluctuations above the wall within a region of interest. To this end, we will employ a generative adversarial network (GAN), together with a fully-convolutional network (FCN) and modal decomposition. Then, we will perform flow control based on deep reinforcement learning (DRL), which will enable discovering novel solutions in terms of flow actuation and design of winglet geometry. In order to assess the robustness of the framework for real-time applications, we will carry out detailed wind-tunnel experiments at KTH.  This framework will constitute a breakthrough in aviation sustainability, and will enable developing more efficient aeronautical solutions worldwide.

Mots-clés : computational fluid dynamics deep learning turbulence turbulent wings
Projet ERC : MitoMeChAnics (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Life Science
Panel : Cell Biology, Development, Stem Cells and R egeneration
Entité participante : Laboratoire européen de biologie moléculaire — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : EMBL
Type d'entité : Recherche
Financement ERC de l'entité : 2200287.0 €

Cell division is fundamental for all life forms and its dysregulation can lead to severe diseases including cancer. The dramatic shape changes eukaryotic cells have to undergo to divide are mostly driven by the cell surface, a complex system that can dynamically modify its mechanical properties. While the importance of physical cues for cell division has long been recognized, the lack of specific tools that modify particular physical properties and bridge molecular-to-cellular scale biophysics prevent us from understanding how cells use their mechanics to regulate form and function. To address this, my group has recently developed a new class of molecular tools that for the first time allows us to manipulate surface mechanics specifically and acutely in living cells. Exploiting this powerful new structural biophysics approach, our first discoveries excitingly demonstrate that the strength of tethering between the plasma membrane and the cortical cytoskeleton is a key control mechanism for cell division, both in cultured cells as well as in mouse embryos. The overarching goal of MitoMeChAnics is to systematically understand how cell surface mechanics controls the different steps of division. My team and I will systematically and quantitatively link cell surface architecture with the resulting mechanics and morphology to determine the structure-function relationship at the cellular periphery in space and time. To this end, we will deploy novel molecular tools and combine them with cellular biophysical measurements, super resolution microscopy and in situ cryo-electron tomography. Moreover, we will build data-driven theoretical models to unravel the physical principles that control the membrane-cortex interface, and test their predictions with novel optogenetic tools. Our project thus takes a highly interdisciplinary approach, combining mechanobiology, molecular engineering, structural analysis and theory to decipher how cells their mechanics to control mitosis.

Mots-clés : cellular biophysics,surface mechanics,abscission,cytokinesis,molecular engineering,afm,cryo et,superresolution,theoretical modelling,mesc,fibroblasts
Projet ERC : OLFAC (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Texts and Concepts
Entité participante : University of Arts and Industrial Design Linz — Pays : Autriche — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1739250.0 €

Smells intervene in human experience. Used by police or military for crowd control, they are a perfidious weapon. Yet, they also offer a wealth of material for intervening arts, the exploration of which is currently gaining traction against a backdrop of climate change and the rise of sexism and racism. So far, however, research on the intervening potential of smell has remained the province of Military Sciences, with research on intervening arts focusing on the visual and the auditory, neglecting the disruptive power of olfaction. OLFAC bridges this gap by, for the first time, exploring the intervening performativity of smell at the intersection of arts and politics. Assuming that odour can be used both to stigmatize and, at the same time, to help “unlearn” socially trained norms, OLFAC will (1) empirically investigate olfactory actors, techniques and technologies applied across performing arts and governmental contexts and (2) develop an integrative, transculturally aligned, and intersectionally oriented theory of Olfactormativity. Fundamentally, the project explores the following questions: How do olfactory techniques and technologies applied in performing arts relate to those used by police and military? What dangers and potential for change do they pose? In what sense are olfactory actors capable of interrupting, subverting or displacing movements of assembly? To what extent can olfactory art break up historically evolved social structures and change perceptual routines? Does the subversive use of odours lead to radical ruptures and changes in terms of gender, class, and ethnicity? Or does it ultimately lead to strengthening conventional concepts of identity? A highly transdisciplinary endeavour, OLFAC draws together approaches and findings from Art-Related Disciplines, Cultural Studies, Anthropology, Psychology, Neurobiology and Chemistry and is set to establish an entirely new playing field for studying the interaction of power, arts, and the senses.

Mots-clés : smell sensory studies intervening arts olfactory arts arts and politics
Projet ERC : AI CARE (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Université Paris Cité — Pays : France — Rôle : PI
Acronyme de l'entité : UP Cité
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1675679.5 €

Despite considerable improvements in short-term allograft and patient survival in kidney transplantation in the past two decades, long-term graft survival remains low, with 20% of transplants failing within five years. Rejection remains a leading cause of graft loss, with immediate consequences for patient morbidity and mortality, posing a major burden on healthcare systems. Increased allograft longevity requires improved patient monitoring and rejection risk stratification using mechanistically informed, non-invasive biomarker strategies and precision diagnostics. The main limitation for such an endeavor is the lack of deeply phenotyped cohorts. Given the complexity and heterogeneity of allograft rejection, patient cohorts with sufficient volume, velocity, variety and validity of data are critical in order to capture disease activity, disease stage, improve risk stratification and optimize treatment. Another limitation is the paucity of multidimensional and integrative research strategies. The goal of the ERC AI-Care project is therefore to go beyond the current state of the art by establishing a transplant precision medicine system to 1) facilitate personalized, non-invasive patient monitoring using cell-free DNA technology (Liquid biopsy) 2) improve rejection diagnosis beyond conventional histology using molecular technologies and digital pathology, and 3) gain further mechanistic insights and improve diagnostic accuracy of transplant organ diseases and apply this knowledge to new research areas.
Projet ERC : Born Once - Die Once (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Human Mobility, Environment, and Space
Entité participante : Université du Danemark du Sud — Pays : Danemark — Rôle : PI
Acronyme de l'entité : SDU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2000000.0 €

Fertility and mortality fuel population renewal. Fertility theory struggles with a multitude of underlying, entangled dimensions, whereas mortality theory can build on strong regularities. Intriguingly, strong regularities analogous to death begin to emerge for birth, shown with my novel born once—die once approach. The Gompertz Law of death appears to apply to birth as well. The linear rise in record life expectancy seems to be mirrored by a linear rise in record ‘birth expectancy’ over decades. Thus encouraged, I propose to harness the power of mortality modeling to (1) add an unexplored, innovative dimension to fertility modeling and (2) develop a shared theoretical framework of birth and death trajectories that applies across the social sciences by conceptualizing different ‘types of individuals’ that are ‘born’ and ‘die’. I strive to achieve Ambition 1 by investigating birth with eight established regularities of death, modeling emerging patterns, and extending existing models of population renewal. Broadening the scope, Ambition 2 applies the approach of Ambition 1 to case studies of couples, families, and households, which I consider ‘social individuals’. I will define types of social individuals along shared dimensions and components rooted in cross-disciplinary perspectives of what defines an individual. Much will be learned from feasible empirical analysis in (1) and (2) about new relationships that can support and invigorate existing formal and empirical demographic research. If I master the conceptual challenges of this proposal, a shared and mirrored theoretical framework of birth and death will contribute a new and powerful toolbox for formal demographic analysis. Together with a generic concept of individuality, it would pioneer a new, transdisciplinary field of research that synergistically investigates the basic principles of formation, dissolution, and renewal, strengthening the theoretical foundation of population sciences.

Mots-clés : formal demography,mathematical demography,mortality,mathematical modeling,theory development,age trajectories of mortality and fertility,birth,death,individual,fertility,couple,family,household
Projet ERC : MIMET (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Life Science
Panel : Environmental B iology, Ecology  and E volution
Entité participante : Université du Danemark du Sud — Pays : Danemark — Rôle : PI
Acronyme de l'entité : SDU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999760.5 €

This proposal aims to gain a fundamental understanding of the impact of anthropogenic conductive particles on methane emissions. We recently showed that conductive particles are vital for the interspecies cross-feeding of a methane-producing consortium from the Baltic Sea. Previous to that, we showed that conductive particles accelerate methane production in synthetic dual-species consortia that typically function via direct electron transfer. Others showed that conductive particles may also stimulate anaerobic methane oxidation. For the latter, the reports are scarce and contradictory. It is mysterious how microorganisms interact with the conductive particles and how conductive particles affect the methane cycle in the environment. These knowledge gaps we will study here. We will use synthetic consortia, enriched environmental consortia, and whole sediment cores. We will identify marker genes for microbe-particle contacts by combining expression studies with targeted gene-deletion and physicochemical cell surface studies. The marker genes we can then use to track similar microbe-conductive particle associations in the environment. We will examine the link between increased anthropogenic particle input and methane emissions in environments from the Mediterranean to the Arctic where higher particle input is likely. I expect to deliver fundamental knowledge about the microorganisms involved in methane transformations by anthropogenically derived conductive particles.

Mots-clés : interspecies interactions microbe mineral interactions extracellular electron transfer direct interspecies electron transfer electromicrobiology
Projet ERC : CHORDS (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Université du Danemark du Sud — Pays : Danemark — Rôle : PI
Acronyme de l'entité : SDU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999498.0 €

Decades of research in computing led to high-level programming languages that can turn a programmer's ideas into executable software. For applications that run in isolation, we have a solid toolbox. We can write code with abstract concepts like functions and objects, and then a compiler tool can automatically produce working implementations. This significantly increases productivity, facilitates program analysis, and reduces the risk of bugs.  Today, society increasingly relies on distributed systems for work, services, social participation, and so forth. Here the key high-level concept is that of communication: a message exchange between independent programs. Yet it is nowhere to be found in the syntax of modern programming languages. Instead, we manually write lower-level instructions for sending and receiving messages in separate programs, hoping that they match each other during execution and produce the wished communications. This is infamously hard, leaving us with code that is difficult to write or trust.  This situation is not due to lack of trying. Choreographic programming is a recent attempt at a programming paradigm with high-level abstractions for communications and their composition in programs called choreographies. Unfortunately, compiling choreographies is technically challenging, which resulted in excessive simplification. Current theories make unrealistic assumptions -- remaining confined to toy scenarios -- and reasoning methods for choreographies are underdeveloped.  This project will break new ground by developing (a) new linguistic constructs for choreographies that can deal with realistic scenarios, (b) logics for verifying properties about choreographies and understanding what they mean for the compiled code, and (c) a toolchain for compiling, testing, and deploying software based on our choreographies. These actions have the potential to significantly impact how we will model and program distributed applications in the future.

Mots-clés : choreographic programming,choreography
Projet ERC : PALEONILE (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth System Science
Entité participante : LIAG Institute for Applied Geophysics — Pays : Allemagne — Rôle : Partner
Acronyme de l'entité : LIAG
Type d'entité : Recherche
Financement ERC de l'entité : 268272.5 €

Over a century of paleontological investigation in Africa has revealed a rich Pleistocene fossil record that includes the evolution of hominins and their material cultures. However, the vast majority of fossil sites are located in the East African Rift Valley (EARV), and our knowledge is heavily skewed by this geographic bias. Poor continental geographic sampling means we lack an understanding of faunal regional variations, and the role of dispersal and geographic variation in the emergence of modern ecosystems. Furthermore, many have questioned the role of the Nile, the longest river in the world, in promoting faunal and cultural dispersal between Subsaharan and North Africa, and beyond to Eurasia. For decades such questions have been answered speculatively, with little data to stand on. PALEONILE is an ambitious project that will address these major gaps in our knowledge through large-scale surveys to reveal a new fossil record from the Middle Nile River Basin in Sudan. This project will test an overarching hypothesis of Pleistocene zoogeographic regionalization in the Nile Basin with respect to the EARV and surrounding areas, and will use an interdisciplinary array of paleontological, geological, geochronological, and archaeological approaches to reach its objectives. The geographic scale of the project is large and the techniques are cutting edge, including high-risk experimental methodologies such as paleobiomolecular recovery and new developments in sedimentary dating. PALEONILE forms the first ever large-scale systematic paleontological project to be conducted in Sudan, where the Cenozoic fossil record remains largely undiscovered, and its potential overlooked. PALEONILE will generate a new paradigm of zoogeographic dynamics and evolution in the African Pleistocene that represents a new synthesis of hydrographic, phylogenomic, archaeological, and paleontological evidence.
Projet ERC : Transformation (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
Entité participante : Princess Máxima Center — Pays : Pays-Bas — Rôle : PI
Type d'entité : Autres
Financement ERC de l'entité : 1887124.59 €

A crucial milestone in tumor progression is the transformation from a benign adenoma to a malignant cancer, which has profound impact on clinical outcome and therapy design. Despite its importance, it is poorly understood why it manifests itself as a bottleneck that can restrain precancerous adenomas for years. While recent sequencing efforts inform ‘static’ insights into the status of cancer genomes, these are incomplete to infer how they evolved, assess functional consequence of mutations, nor how much malignancy is environmentally instructed.  Practical limitations hamper scientific advancement into the dynamic nature of tumor evolution. Foremost, studying human tumor evolution is challenging as sequential sampling is not feasible, and pre-malignant events that may hold the clue for understanding transformation often occur years before cancer diagnosis. Moreover, experimental models that reflect malignant transformation, let alone of human origin, are lacking.  TRANSFORMATION aims to resolve how human cancer genomes evolve towards malignancy, and its role in triggering malignancy.  To gain new insights on this matter, TRANSFORMATION takes a unique approach to produce experimental insights into evolutionary parameters that are inherently dynamic in nature and cannot be obtained from tumor sequencing alone. Using paired biopsies from early-stage colon cancers, I generated unique organoid models from pre- and post-transformation regions. Together with in situ analysis of matched tumors, TRANSFORMATION will reveal the elusive rate and order by which mutations and copy-number alterations are acquired, the patterns by which cancer karyotypes evolve, dissecting functional from non-functional genetic heterogeneity on tumor phenotypes and investigate possible selection pressures imposed by environmental conditions. TRANSFORMATION will solve how colon tumors evolve pre-malignancy, which properties promote malignancy and why transformation is an evolutionary bottleneck.

Mots-clés : colon cancer,adult stem cells,organoids,chromosomal instability,tumor evolution,malignant transformation,pre malignancy
Projet ERC : IONIAN (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and Communication Engineering
Entité participante : Vrije Universiteit Brussel — Pays : Belgique — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999403.75 €

Advancements in sensors and deep learning have elevated the perception capacity of machines, bringing mid-level autonomy within reach. However, the abundance of high-dimensional data, including video and dynamic point cloud streams, strains current storage and communication technologies to their limits and curtails the ability of machines to collaboratively perceive the environment, a critical factor for achieving safety and the ambitious goal of high-level autonomy. State-of-the-art cooperative perception methods are based purely on a data-driven approach, requiring massive training data and computational resources, and lacking interpretability, explainability, and a solid theoretical foundation. This proposal puts forth a groundbreaking multiterminal coding paradigm for intelligent machines enabling data compression and communication systems that break the current limits of the predictive coding archetype. It builds a unique concept that unifies traditional distributed source coding and signal processing domain knowledge with modern deep learning. First, it leverages machine learning to solve long-standing problems in multiterminal coding theory and devise code constructions achieving the fundamental limits, thereby establishing a theoretical framework that defines the amount of information required to be sent per agent to solve the cooperative perception task. Second, it leverages domain knowledge to drive the design of interpretable and data- and parameter-efficient machine learning models for cooperative perception. Third, it reinforces this interplay by pioneering explanations that enforce and assess the interpretability of the designed models. IONIAN will have a profound impact on the way intelligent machines, including ground and aerial vehicles, and mobile robots, compress and communicate multi-sensory data to collaboratively perceive the environment for autonomous safe navigation, ultimately leading to trustworthy operation and acceptance of such systems.

Mots-clés : machine learning for signal processing,cooperative perception,distributed source coding,distributed video coding,multiterninal source coding,video and point cloud processing,multimedia communications,smart transportation systems,autonomous vehicles,intelligent machines