Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : MetaWing (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : Université de technologie de Delft — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : TU Delft
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2374014.0 €

Achieving extensive laminar flow can reduce aircraft drag by up to 15%, offering breakthrough potential for curbing polluting emissions in aviation and other energy-intensive sectors. To achieve this, wave-like flow instabilities growing in laminar boundary layers need to be controlled and attenuated, to delay transition from laminar to turbulent flow. However, their complex, multi-scale, and broadband nature makes these instabilities extremely challenging to control.  In MetaWing I propose a new disruptive concept for flow control, first born in classical wave physics: Metamaterials. These are engineered composite structures, invoking dispersive wave phenomena to gain exotic properties that go beyond what is considered possible in Nature. The main such property I want to explore is the bandgap, a range in which waves are suppressed when interacting with the Metamaterial. My team and I have recently found key evidence of dispersive wave suppression in boundary layers. However, wave-like flow instabilities have key differences from classical waves, forming a new regime of dispersive wave interactions. Thus, the nature of bandgaps in boundary layer flows remains unclear and unexplored. I aim to reveal and understand the formation of bandgaps in transitional fluid flows and use them to suppress wave-like boundary layer instabilities, thus delaying laminar-turbulent transition. To achieve this, I propose the first holistic intersection of Metamaterials and Laminar Flow Control using theory, numerical simulations, novel fabrication methods and state-of-art experimental measurements. The combined results will uncover the complex dynamic behaviour of flow instabilities under dispersive interaction with Metamaterials and will help us understand and fully exploit bandgap mechanisms in transitional flows. MetaWing will create a new class of Metamaterials for flow control and pave the way to ultra-low drag wings for the next generation of emission-free aviation.

Mots-clés : laminar turbulent transition,flow control,transition delay,tollmien schlichting waves,crossflow instability,metamaterials,phononic crystals,acoustic metamaterials
Projet ERC : ARCS (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and Communication Engineering
Entité participante : Université de technologie de Delft — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : TU Delft
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1996040.0 €

Autonomous robots such as autonomous vehicles cars and drones have the potential to revolutionize the way we work and live. Unfortunately, current autonomous robots do not have ‘common sense’ and may enter a catastrophic condition called loss-of-control after failures. The ultimate goal of this research is to enable a new generation of autonomous robots that are aware of their physical capabilities and limitations, allowing them to act with common sense after failures. To achieve this I propose a new paradigm in autonomous robot control: ‘Artificial Physical Awareness’ (APA). APA requires accurate real-time knowledge of the time-varying stochastic safe envelope which is a subset of the state-space inside which safe operations of the autonomous robot can be guaranteed. The safe envelope is stochastic and time-varying; it contains uncertainties and will shrink after failures, reflecting the reduced post-failure performance of the autonomous robot. Obtaining and utilizing the time-varying stochastic safe envelope in real-time represents a currently unsolved scientific challenge for the following reasons: 1) the safe envelope cannot be measured directly; 2) current safe envelope computation methods are real-time intractable and/or do not take into account uncertainties; and 3) no control methodology exists that allows for time-varying safe-envelope informed balancing of safety and performance. This multidisciplinary research combines new insights in time-varying stochastic state reachability analysis, tipping-point forecasting, bio-inspired envelope sensing and recovery, and nonlinear fault-tolerant control to develop the new APA-autopilot system, which is the main output of this research. This research project has the potential to lead to a revolution in autonomous robot design, and operations by providing transparent safety and performance bounds, even after failures.

Mots-clés : safe autonomy,aerial robotics,bio inspired robotics
Projet ERC : CURE (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : ARMINES — Pays : France — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 1440725.0 €

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 : Dark100 (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe Sciences
Entité participante : Université de la Ruhr à Bochum — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2331823.0 €

Dark100 will shine a light into an underexplored dark matter particle mass range. Driven by recent theoretical developments, both in tools and motivation, Dark100 will search for dark matter particles between 100 TeV and several tens of PeV. Leveraging novel, cost-effective telescope technology, Dark100 will deploy an array of telescopes capable of probing dark matter annihilation, improving on the sensitivity of currently operating and planned gamma-ray instruments by an order of magnitude with unprecedented energy and angular resolution.  Dark100 will build a unique dataset of deep gamma-ray observations of dark-matter-rich astrophysical systems. Dark matter will potentially be detected, or in the event of non-detection, limits on its velocity-weighted annihilation cross section will be set. For a non-detection, dark matter will be ruled out for part of the probed mass range and theoretical models constrained for the full probed mass range.  The impact of Dark100 will be felt beyond the dark matter community. By demonstrating a new gamma-ray telescope paradigm, Dark100 will enable a range of possible astrophysical studies with gamma rays above 100 TeV, including searches for Galactic Pevatrons and transient events. Its public data archive will encourage synergy with other gamma-ray instruments.  Dark100 takes advantage of a unique moment in theoretical and technological development that enables its pioneering science goals. While the theoretical motivation is fully developed and the technology is well-tested, the deployment of the Dark100 array and collection and interpretation of its observations is an ambitious project that demands the resources of an ERC consolidator grant. The PI's leadership in dark matter searches and gamma-ray analysis and simulation make her ideally suited to direct this program.

Mots-clés : very high energy gamma ray astronomy,cherenkov astronomy,dark matter searches with gamma rays
Projet ERC : TDP-Assembly (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Life Science
Panel : Molecules of Life: B iological  Mechanisms , Structures  and Functions
Entité participante : Université Johannes-Gutenberg de Mayence — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : JGU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1919989.0 €

Neurodegenerative disorders are an enormous societal burden and we lack therapies that target these diseases at their origins. To develop therapies, we need to understand what goes wrong at the molecular level. I uncovered key mechanisms that cause RNA-binding proteins, such as TDP-43, to dysfunction and drive neurodegenerative processes. I discovered that RNA-binding proteins self-assemble into ribonucleoprotein granules that are the likely origins of RNA-binding protein aggregates. More recently, my group and I revealed aberrant phase transitions of condensed RNA-binding proteins occurring in disease and identified fundamental mechanisms by which such phase transitions are regulated in cells. In TDP-Assembly, I now want to find out why these proteins exhibit a self-assembly behavior that apparently risks pathological aggregation. My hypothesis is that self-assembly is essential for their many functions in gene regulation, and that different types of self-assemblies, e.g., small clusters or fluid or solid condensates, mediate different functions in cells. Using TDP-43 as a paradigm, I will test this hypothesis to ultimately understand the molecular basis of RNA-binding protein dysfunction in neurodegeneration. To achieve this goal, I will use synthetic biology approaches to rationally tune self-assembly of TDP-43 in cells. I will study how altered TDP-43 self-assembly affects its known molecular functions, i.e., regulation of transcription, alternative splicing, and translation. Transcriptome and proteome analyses will draw a systems biology map of altered TDP-43 self-assembly and might lead us to novel functions of TDP-43 self-assembly. Ultimately, I will address how TDP-43's self-assembly, and thus its functions, are altered by disease-linked mutations in neuronal cells. TDP-Assembly will forge a new understanding of the functional and pathological relevance of RNA-binding protein self-assembly and might inspire new therapies that target self-assembly processes.

Mots-clés : rna binding proteins,intrinsically disordered proteins,phase separation,neurodegeneration,tdp 43
Projet ERC : PREDDYCT (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth System Science
Entité participante : Université polytechnique de Catalogne — Pays : Espagne — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 1593944.25 €

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 : DORMATRIX (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Materials Engineering
Entité participante : Biogipuzkoa Health Research Institute — Pays : Espagne — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 2349943.0 €

Cancer dormancy and the trigger to transition to active metastatic growth is a big open question. Current in vivo models focus on niche-specific cell and molecular mechanisms, ignoring biophysical aspects. Dormancy evolves with complex spatio-temporal dynamics; yet, there is a knowledge gap in the understanding of the heterogeneity of the units, and the dynamics of their interaction and evolution. Emergence occurs when a critical mass of units synergistically communicates giving rise to a new macro-level organization, with properties greater than the sum of the units. Engineering emergent phenomena in biological systems is a big research challenge as they originate from multiscale communication. In DORMATRIX, I propose a radically new view. I hypothesize that the balance between cancer dormancy and “awakening” is a collective emergent phenomenon, whereby a critical mass of micro-units communicates with each other and the environment in order to transition to metastatic growth. The main objective of DORMATRIX is to engineer breast cancer (BC) dormancy as a collective emergent phenomenon using biomaterials-based dormancy-on-a-chip devices. My previous data shows that we can (i) apply biophysical cues to control BC proliferation and (ii) visualize in vivo early BC bone metastasis. I will now address this outstanding challenge with a multidisciplinary approach and 1) apply biophysical principles with novel biomaterials to model in vitro cancer micro-units, 2) develop advanced 3D imaging to visualize collective cancer dormancy and bone microdamage, 3) develop in silico models based on evolutionary game theory to predict the dynamics, and 4) prove my hypothesis with dormancy-on-a-chip devices. Understanding the critical mass and multiscale communication required for emergent phenomena will enable the development of novel therapies to delay or prevent metastasis. The resulting technology for engineering emergent phenomena will spark research on other biological systems.

Mots-clés : biomaterials biomedical engineering hydrogels biophysical properties advanced 3d imaging microfluidics physical science of cancer
Projet ERC : ROOTS (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : Libera Università Internazionale degli Studi Sociali — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 516875.0 €

The aim of this project is to understand the historical roots of global inequality. Specifically, it will address two questions: Why did some regions experience an early start in the development of complex hierarchies? How relevant is such an early transition in explaining current comparative economic development. A series of theories, going back to the origins of the social sciences, emphasize the role of some crucial environmental factors, in explaining the rise of the very first complex hierarchies. Unfortunately, however, the scarcity of global datasets on natural environment and hierarchy, going back to the first human societies, has made it difficult to test these theories. Partial empirical validation has come almost exclusively from either cross-sectional empirical studies, using data on the current environment to proxy for past conditions, or case studies focusing on a limited numbers of well-studied areas. This project aims at filling this gap by combining new global panel data on ancient climate (a result of recent advances in paleoclimatology), ancient migrations (reconstructed through archaeogenetics) and ancient societies (constructed from aggregating archaeological reports) with advanced econometric techniques. The project comprises three lines of research. The first line will test a prominent theory that relates a series of changes in the natural environment with the emergence of intensive food storage and the subsequent rise of the first relevant socio-economic inequalities among hunter-gatherers. The second line will focus on farming societies and investigate the role of long-distance trade, spurred by the invention of bronze, on the rise of the first urban civilizations and new unprecedented economic inequalities among farmers. The third line will document the long shadow that ancient hierarchies (either among hunter-gatherers or farmers) have produced on current comparative economic development.

Mots-clés : long term persistency,comparative development
Projet ERC : MotorAdapt (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Imperial College London — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : ICL
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1993474.0 €

Animals and humans learn some motor skills very slowly: for example, it takes about a year for a baby to learn to walk. In comparison, motor adaptation can be very fast. For example, the first time someone walks on snow, it might take them a little time to get used to the new conditions, but not a whole year. And adaptation depends on previous acquired skillset. Mastering tennis for instance increases how fast squash is learned, but also some “bad habits” from tennis have to be unlearned, and switching to squash for too long can hurt one’s performance in tennis. But how the brain learns different motor skills is still unknown. The main reasons are 1) it is very hard to record single neural activity for the time it takes to learn a new skill, and 2) bottom-up methods from computational neuroscience are very precise for modelling neurons and synapses (i.e., the connections between neurons), but fall short when it comes to learning behaviourally relevant tasks. In contrast, methods from machine learning are becoming increasingly more powerful.  My innovative idea is that instead of focusing on skill acquisition, which is slow, I propose to focus on rapid adaptation where recordings during its whole duration is feasible. I propose to use data-driven machine learning methods alongside newly acquired motor datasets to uncover where changes occur in the brain during motor adaptation, which brain regions are involved, which rules govern the changes, and how different motor skills interact. Unravelling the fundamental mechanisms underpinning rapid motor adaptation will equip me with the knowledge necessary to engineer tools that accelerate motor adaptation, particularly for medical applications. 100 million people in the EU alone suffer from a disability including movement disorders. I propose to leverage these tools to accelerate how quickly users can adapt to intracortical Brain-Computer-Interfaces to improve quality of live and increase patient adoption rate.

Mots-clés : computational neuroscience
Projet ERC : off-coustics (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : Université polytechnique de Madrid — Pays : Espagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1992500.0 €

For renewable energies to be sustainable in the future, their impact and harmful effects on the environment should be minimum. Recent evidences suggest that offshore wind and tidal turbines can have an acoustic damaging impact on marine life, due to the sustained generation of noise, which propagates very efficiently underwater.  Off-coustics combines numerical simulations and experiments to provide insights into the physics governing the aero/hydro-acoustic generation and propagation for offshore wind and tidal farms. Control of these physics will enable the design of silent offshore farms enabling renewable energy with zero acoustic impact. First, I propose to develop a novel aero/hydro-acoustic solver, blending advanced high order numerical techniques through machine learning and trained with experiments, to simulate flow-acoustic signatures for wind and tidal turbines, in realistic offshore environments (including bathymetry, air-water surface, etc.). Second, an experimental campaign will generate aero/hydro-acoustic data for scaled turbines and farms to help elucidate the physics governing offshore acoustics and to guide/validate the flow-acoustic simulator. Third, simulations and experiments will be combined to characterise turbines in complex offshore environments and to develop physic-informed surrogate models. Fourth, using the developed surrogate models and optimisation, Off-coustics will propose new designs of silent farms that minimise the acoustic impact while ensuring energy production.  Major advances in multidisciplinary aspects are expected, including fluid mechanics, numerical simulations, optimisation, experimental acoustics, aero/hydro-acoustics and offshore wind and tidal turbine physics.

Mots-clés : computational fluid dynamics,high order methods,optimisation,machine learning,offshore wind turbines,tidal turbines,acoustics