Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : ML Transport (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth System Science
Entité participante : Université de Tel Aviv — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2422688.0 €

Although crucial in determining the oceanic distribution of heat, biological nutrients, carbon, and pollutants, the circulation dynamics that governs oceanic vertical transport processes of physical and biogeochemical properties through the near surface layer (i.e., mixed-layer; ML) are yet to be comprehensively quantified. Recently we and others have demonstrated that submesoscale currents (SMCs) -- newly discovered flow structures consisting of fronts, filaments, and eddies -- have a strong influence on these exchange processes, to be fully explored and characterized. The high spatiotemporal variability of SMCs and the complex physics that determines their interactions with other ML phenomena like surface gravity waves and near-inertial waves, renders in situ measurements of these processes extremely difficult to obtain. Furthermore, current climate models lack the grid resolution and variable forcing components required to adequately represent ML physics, making it one of the greatest uncertainties in climate projections. The proposed research will address this critical gap through three objectives: 1) develop the numerical capability to simulate ML physics in a realistic inhomogeneous environment while resolving boundary layer turbulence and wave dynamics; 2) develop a theoretical framework for a physics-based parametrization of ML vertical exchange rates; and 3) directly measure turbulent mixing, tracer distribution, and transport rates near SMCs in situ, providing the crucial observational support necessary to guide and fine-tune the parameterizations. To achieve this goal, we will extend our frontogenesis theory, analyze particle and tracer concentrations in carefully designed realistic and nested large-eddy simulations, and examine drifter trajectories and passive tracer spreading in multi-asset field campaigns. This synergistic approach will substantially impact oceanic biogeochemical modeling, pollutant transport mitigation, and climate projections.

Mots-clés : physical oceanography,ocean observations,climate processes
Projet ERC : PoPPiE (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : Université de Southampton — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1498930.0 €

To understand the levels of dissatisfaction with democratic performance in Europe, as well as the most effective reforms for addressing it, requires an understanding of citizens’ process preferences: how do they want democracies to function? Empirical research on process preferences is rapidly proliferating, but it has been underpinned by an assumption that the task of this research is to discover which one of a set of abstract, normative models of democracy citizens subscribe to. The driving idea behind PoPPiE is that this assumption is a serious limitation. It has led the study of process preferences to focus on discrete choices between simple alternatives that fail to represent the real choices facing the inhabitants of complex democratic systems and neglected the extent to which individuals are conflicted between competing democratic norms. It prevents robust inferences on important questions, such as levels of polarisation and support for democratic innovations.  PoPPiE develops a groundbreaking new approach to rethink the conceptual, ontological and methodological foundations for researching political process preferences. - First, by applying democratic systems theory to re-conceptualise the content of process preferences it will generate a wholly new conceptual understanding of preferences for complex democratic systems, rather than ideal models of democracy. - Second, instead of assuming that the nature of process preferences is inherently ideological, PoPPiE uses a novel multidimensional theoretical framework to assess levels of contextuality, conditionality and coherence in process preferences. - Third, it develops a new mixed methods approach, producing a unique dataset that combines qualitative and quantitative data on a single individual’s preference.  The new approach will not only strengthen the foundations of the science of process preferences, but ensure its findings are more policy relevant for the project of renewing European democracies.

Mots-clés : political process preferences conceptions of democracy political attitudes democratic systems
Projet ERC : AMMicro (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Materials Engineering
Entité participante : Société Max-Planck pour le développement des sciences — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : Société Max Planck
Type d'entité : Recherche
Financement ERC de l'entité : 1498356.0 €

Current 2.5D microelectromechanical systems (MEMS) devices are disadvantaged by their distinctive unreliability. Lack of built-in damage sensing, impact protection mechanisms and the absence of application-relevant reliability tests, collectively mask the true potential of MEMS devices. AMMicro will address these limitations by designing and developing the building blocks essential for robust next-generation 3D MEMS devices. This will be done using a novel combination of cutting edge electrodeposition technique and advanced reliability testing protocols.  Localized electrodeposition in liquid (LEL) is an advanced micromanufacturing technology, capable of printing 3D metal micro-/nano-architectures. With recent developments in advanced reliability testing using micro/nanomechanical testing (MNT) platforms, application-relevant high dynamic conditions are possible, yet remain under-exploited.  AMMicro will break new ground by harnessing the combined potential of LEL and MNT. Multimetal microlattices will be fabricated with optimized position-specific chemical compositions to maximize specific impact energy absorption. Multiphase microlattices fabricated with dyed fluid encapsulations and pressure-release valves will enable novel self-damage sensing and impact-protection mechanisms. Full-metal 3D MEMS based load sensors will be fabricated and used for tensile testing of LEL printed nanowires. The enhanced reliability of these microarchitectures will be validated using application-relevant advanced mechanical testing.  AMMicro is a highly interdisciplinary project at the boundary of materials science, mechanical, electrical and manufacturing engineering. For the material science community, it will pave the way for breakthroughs in critical applications including catalysis, phononics, photonics, etc. Beyond materials science, it has the transformative potential to revolutionize several fields including drug delivery, microscale temperature sensors, etc.
Projet ERC : ValueIn (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Soci al World and Its Interactions
Entité participante : Université de Tel Aviv — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499750.0 €

The EU aims to promote common values and beliefs among members. Classic research establishes parents as the main role models for such transmission. However, children today learn values from new role models, such as social media influencers (SMIs), users who share content to acquire followers. The project will construct a comprehensive value transmission theory applicable across contexts and adapted to the digital age. It will define essential value transmission aspects: source, channel, message, receivers. The source studied will be mothers and SMIs. The channel will be conversations and online exchange of content and engagement. The message will be a set of values motivating prosocial behavior, including but not limited to moral values, and the balance among them. Explicit and implicit messages will require varying processing systems. Child and adolescent receivers will be active partners, choosing, negotiating, and transmitting values. I will investigate value transmission practices used by interaction partners to convey messages; I will test the effectiveness of practices in promoting value similarity, moral reasoning and prosocial behavior; Finally, I will examine interactions between mothers’ and SMIs’ value transmission processes. In 7 studies, I will (1) longitudinally study mother-child conversations on prosocial behavior in daily life and in social media. Conversations will be analyzed thematically, longitudinally across 2 years, and moment-to-moment; (2) analyze values in SMI content and investigate their relations with follower engagement, applying manual and artificial intelligence coding; (3) Last, I will experimentally test the effectiveness of value transmission practices, including cases of source disagreement. The project sets the stage for research into value transmission across contexts and cultures, broadens understanding of the role of family and social media in development, and creates a new standard for research methods in child development.

Mots-clés : values,value transmission,parenting,social media influencers
Projet ERC : SHAPE (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Université Adam-Mickiewicz de Poznań — Pays : Pologne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499750.0 €

Functionalities of enzymes are encoded in amino acid sequences and directed by their SHAPEs with complementary binding pockets for specific substrates. Natural enzymes are remarkable catalysts, however, they are typically optimized by evolution to operate under the constraints of the physiological environment of a living system, which strongly limits the scope of their applications in organic synthesis. Here, I propose to develop abiotic enzymes to selectively catalyze chemical transformations in non-physiological environments. The main objective of the project is to use monomer sequence control to fine-tune the SHAPE of abiotic macromolecules to obtain the desired catalytic functionality. This goal will be realised via four work packages: (I) Primary structure control to input information into macromolecules – development of synthetic methods yielding high molar mass, sequence-defined polymers, to deliver abiotic proteins at high scales and numbers. (II) SHAPE control by single chain folding and topology – secondary and tertiary structure evolution by varying the monomer sequence and stereochemistry to tune intramolecular interactions, leading to controlled engineering of globularly folded polymers. (III) Introducing catalytic activity into abiotic polymers – enhancing selectivity and efficiency of catalytic reactions by advancing an outer sphere that surrounds the metal cofactor. (IV) Sequence-function studies using machine learning – delivery of models able to interpret multivariate data that will guide the development of complex catalytic systems to find and predict dependencies inaccessible by conventional methods. Our approach proposes an unexplored method for obtaining abiotic, sequence-defined polymers operating in a non-biological environment whose functions can rival those of natural macromolecules. The study will reveal valuable information on sequence-dependent properties of polymers, to open a field of abiotic enzymes for organic transformations.

Mots-clés : macromolecules,sequence defined polymers,single chain folding,molecular topology,catalytic functions
Projet ERC : NanoNonEq (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed Matter Physics
Entité participante : Université de Tel Aviv — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

At the core of far-from-equilibrium biological activity lies an orchestra of molecular motors, constantly dissipating energy while converting chemical fuel into mechanical work. Estimating the amount of the free energy budget lost to dissipation is crucial for a deeper understanding of the underlying nonequilibrium dynamics and for unravelling the thermodynamic constraints on the possible biological processes. Although there are theoretical tools for quantifying nonequilibrium activity and dissipation in the framework of stochastic thermodynamics, there is a gap between these analytical calculations and their experimental applicability. The difficulty stems from the limited accessibility to the myriad degrees of freedom of complex systems and the finite measurement resolution, which can mask the footprints of nonequilibrium dynamics, such that they may appear as passive thermal fluctuations. I will address this challenge both experimentally and theoretically. In my lab, I will develop fluorescent nanosensors for unveiling microscopic activity otherwise inaccessible in complex biological systems. Fluorescent single-walled carbon nanotubes with tailored functionalization will transduce molecular-motor activity to a modulation of the emitted fluorescence, providing a novel degree of freedom never before exploited as a phase-space coordinate for inferring dissipation in nonequilibrium systems. I will incorporate the nanotube sensors in minimal biomimetic models of active systems, including DNA-gel and reconstituted cytoskeleton driven by molecular motors, to demonstrate my approach in a highly controlled environment. Further, I will internalize the nanotubes within live cells, and utilize the fluorescence signal to estimate the dissipation in nonequilibrium intracellular organization. In parallel, I will advance theoretical tools for estimating the dissipation from experimental data, based on an approach I have pioneered for detecting time-irreversibility.

Mots-clés : nonequilibrium statistical mechanics,stochastic thermodynamics,single walled carbon nanotubes,fluorescent nanosensors
Projet ERC : 2CE MECP2 (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Université de Tel Aviv — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Epigenetic signalling pathways are required to translate external sensory input to neuronal gene modulation and function, and disruption of epigenetic signals leads to devastating brain pathologies. One prominent example is Rett syndrome (RTT), a postnatal neurodevelopmental disease which results in rapid deterioration of sensory, motor, cognitive, and social functions. RTT is caused by loss of function mutations in a single gene encoding for Methyl-CpG binding protein 2 (MeCP2), an abundant and multifunctional methylation reader in the brain. While transgenic mouse models of MeCP2 loss have provided significant insights into RTT, they also revealed the vast complexity of the regulation of MeCP2 signalling and cell- specific heterogeneity. Importantly both a deficit and a surplus of MeCP2 give rise to pathological phenotypes. Here, we will develop an imaging-based approach to monitor endogenous MeCP2 signalling and dynamics in intact neuronal circuits in awake behaving mice. We will combine CRISPR/Cas9 genome editing to fluorescently label endogenous MeCP2 with FRET based biosensors to detect dynamic MeCP2 signalling without perturbing its innate regulation. In vivo two-photon fluorescence lifetime imaging will enable dual imaging of MeCP2 signalling and concurrent neuronal activity. Using this approach, we will map the functional landscape of MeCP2 activity in the intact mouse brain across cell-type, circuit, and sensory experience. We will also image in vivo MeCP2 signalling in transgenic mice with common RTT mutations. This will allow us to detect early cell-type specific MeCP2 dysfunction and avoid broad late-stage RTT symptoms. The direct visualisation of the intricate interplay between MeCP2 signalling and neuronal function within intact neuronal circuits will be transformative since it will shed light on the physiological role of epigenetic signalling in the brain and provide vital insights to future therapeutic interventions.

Mots-clés : neuronal signalling,mecp2,in vivo imaging,fret,2pflim
Projet ERC : DigiWeB (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Soci al World and Its Interactions
Entité participante : Queen Mary University of London — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : QMUL
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499763.0 €

DigiWeB is the first study of how Artificial Intelligence (AI) is merging welfare systems and migration management. Governments are increasingly relying on AI to make decisions about who gets welfare benefits. This is important because AI combines large amounts of data and makes predictions based on "risk factors," which can be discriminatory, especially against marginalized migrants.  DigiWeB takes a broad, interdisciplinary approach and makes four contributions:   1. It pushes the frontiers of research on digital welfare systems, explaining how AI and connected databases work to make decisions about welfare.  2. It introduces a new way of studying AI’s impact by combining data science and social science, particularly in the context of migration management. 3. The project’s approach can be applied to other fields, like policing or counterterrorism, to understand the broader effects of AI. 4. It highlights a significant shift in how governments operate, showing how welfare systems are changing due to AI and digital technologies, with international implications.  DigiWeB has three main aims:  1. To understand how welfare-AI functions and influences welfare decisions. 2. To explore how these decisions are implemented by social workers and how they impact migrants. 3. To theorize the international effects of welfare-AI.  The project uses both data science to study welfare algorithms and social science to explore how they impact people in practice across three countries (Netherlands, Sweden, UK). It will provide a major advance in understanding modern government, as AI is transforming welfare provision and migration management faster than current scholarship and policy can respond. By extension, DigiWeB will offer practical improvements for responsible welfare-AI.

Mots-clés : migration,borders
Projet ERC : FASTER (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental Constituents of Matter
Entité participante : Université Heriot-Watt — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2453025.0 €

Ultrafast laser pulses allow us to follow fundamental processes such as chemical reactions and electron transport at their natural timescale. Because optical (ultraviolet, visible or infrared) laser sources have not been able to reach the attosecond pulse duration required to investigate the fastest events, ultrafast science has moved to extreme-ultraviolet (XUV) wavelengths. However, XUV photon energies are far higher than the scale of chemically and electronically relevant valence-electron excitations, so XUV spectroscopy is at most only indirectly sensitive to some of the most important interactions. Furthermore, the low pulse energy of attosecond XUV sources has so far prevented experiments with true attosecond time resolution. In FASTER, I will push far beyond the limits of conventional laser sources and bring attosecond time resolution to the optical domain. Using advanced optical soliton dynamics in gas-filled hollow capillary fibres, I will create ultrabroadband supercontinuum probe pulses and wavelength-tuneable pump pulses from the vacuum ultraviolet (100 nm) to the near infrared (1000 nm) with both attosecond duration and sufficient pulse energy for attosecond pump-probe studies. Using the flexibility of soliton dynamics and all-optical spatio-spectral manipulation, I will tailor these pulses to specific experiments. I will then take one step further and develop two-dimensional spectroscopy with the same approach, combining ultrabroadband optical attosecond pulses with the ability to identify different excitation pathways and quantum coherences. In collaboration with expert groups, I will apply these new capabilities to some of the most challenging questions in ultrafast science and directly observe crucial valence-electron interactions with unprecedented time resolution. FASTER aims at the physical limit of optical ultrafast laser pulses and a new regime of ultrafast spectroscopy, opening up entirely new ways of observing the fastest processes in nature.

Mots-clés : ultrafast optics,ultrafast science,photonics,nonlinear optics,spectroscopy
Projet ERC : ACTIVATE (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth System Science
Entité participante : Université d'Oslo — Pays : Norvège — Rôle : PI
Acronyme de l'entité : UiO
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499738.0 €

While land-atmosphere exchanges of carbon, water, and energy are key to understanding changes in the Earth system, we still fundamentally lack a methodology to obtain representative estimates of these surface fluxes at the scale of a single grid cell of an Earth System Model (typically 10-100 km), let alone for a wider region. ACTIVATE combines an observing system consisting of a swarm of drones carrying meteorological sensors and gas analyzers, mobile and stationary flux towers, as well as satellites, and fuses their observations with different land-atmosphere models using data assimilation methods. ACTIVATE will develop an adaptive Bayesian Experimental Design framework to generate maximally informative observation strategies for expensive data collection, and adaptively reposition drone swarms during a flight as new observations become available to optimally infer surface fluxes in the landscape. We will demonstrate the framework (i) in idealized synthetic experiments, (ii) at managed and industrial sites with known flux hotspots, and (iii) in targeted high-resolution simulations in poorly represented regions with expensive models that explicitly resolve subgrid-scale processes in Earth System models. We will apply the ACTIVATE framework around existing observatories in vulnerable arctic regions, where the lack of strong observational constraints from state-of-the-art observing systems is particularly apparent and problematic. ACTIVATE will produce: unprecedented observational datasets for new model developments in some of the most data-sparse regions on Earth, uncertainty-aware parameter estimates for critically unconstrained processes, and a pioneering active experimental design framework for terrestrial observing systems. The broader vision of ACTIVATE is to develop active learning capabilities for improved data assimilation in models to elevate our understanding of land-atmosphere interactions across spatio-temporal scales.

Mots-clés : surface gas fluxes,energy exchange,observing systems,data assimilation,active learning