Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : FOODCIRCUITS (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Soci al World and Its Interactions
Entité participante : Université de Barcelone — Pays : Espagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2017409.0 €

Most fruit and vegetables required and enjoyed by European and North American societies would not be possible without the planting, harvesting and transporting performed by migrant labourers. Yet, the contribution of migrants to crucial food systems is generally hidden in the experience of buying and consuming food. In this project, I take asparagus from Germany, oranges from Spain and strawberries from California as the vantage points from which to see connections between migrants and societies as well as the ways in which those connections become invisibilized. I focus on embodied experiences in food circuits in order to understand the labour of producing and transporting food and the process of consuming and incorporating food into oneself. Tracing asparagus, oranges and strawberries through their circulation, I investigate the embodied experiences of migrant farm labourers – who are treated simultaneously as essential, disposable and sometimes prohibited; supply chain workers – who are made up increasingly of migrants and work under tight delivery time constraints; and consumers – who eat with diverse intentions of survival, enjoyment, identity and ethics. I propose a new way of seeing social and embodied connections between migrants and societies – through the beauty, brutality and necessity of food. With FOODCIRCUITS, I will provide novel contributions on three primary fronts: (i) developing a theory of connections between migrants and societies – including on embodied levels – by following fruit and vegetables as they circulate from the hands that pick them, through the arms that deliver them to the mouths that eat them; (ii) re-conceptualizing the ways in which indirect connections between different categories of people are invisibilized through the infrastructures and processes of producing, transporting and consuming food; (iii) expanding collaborative methods and ethics in field research across steep social and power hierarchies.

Mots-clés : food studies,embodiment,well being,labour
Projet ERC : EpiPhys (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Life Science
Panel : Cell Biology, Development, Stem Cells and R egeneration
Entité participante : Centre national de la recherche scientifique — Pays : France — Rôle : PI
Acronyme de l'entité : CNRS
Type d'entité : Recherche
Financement ERC de l'entité : 1999838.0 €

Epithelia operate major biological functions throughout the human body. Forming de novo during preimplantation development, the trophectoderm (TE) is our first epithelium. The TE mediates the formation of the first mammalian lumen, which involves considerable physical challenges. Indeed, pressurized fluid, pumped through the TE, fractures intercellular contacts before cells squeeze this fluid into a single lumen. We recently found that cell adhesion and cell contractility determine the position of this lumen, which then controls further differentiation of the TE. This suggests that physical cues can determine the first mammalian axis of symmetry and TE patterning. However, most physical properties of epithelia controlling this process remain unknown, as their study in vivo has been so far very limited. In this project, we will investigate how the physics of the TE shapes and patterns the mouse preimplantation embryo, by using novel approaches, such as microfluidics and optical tweezers, that allow direct measurement of physical properties in vivo. First, we will determine epithelial transport properties of the TE and their regulation during lumen opening. We will next map plasma membrane reservoirs and membrane mechanics during preimplantation development. We will then characterize how the nuclear envelope and chromatin regulate nuclear mechanics during epithelial thinning. With this broad and unprecedented in vivo physical characterization, we will determine how epithelial transport, plasma membrane and nuclear mechanics influence epithelial stretching and patterning during lumen growth and positioning. Finally, we will determine the relative contributions of these physical properties in this process, and investigate their relationship. Together, we will build an integrated view of how uncharted physical properties of epithelia control stretching and patterning, in the context of a process that is key to the fertility of the ageing European population.
Projet ERC : ProGB (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Materials Engineering
Entité participante : Université de Fribourg-en-Brisgau — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1995938.0 €

Property transformation of grain boundaries (ProGB) is a challenge and a big opportunity. Besides being indispensable in many semiconductor compounds, grain boundaries (GBs) are found to be often detrimental in most bulk and thin-film solar cells limiting devices’ efficiency. This is because understanding how the detrimental properties can be intentionally transformed remains difficult and often impossible. Therefore, ProGB aims to understand and even transform the GBs properties on the nanoscale by employing a novel approach. This approach consists of three steps with three objectives. (1) It will explore the GBs' intrinsic properties and the properties transformation down to the nanoscale in semiconductor prototypes through a new platform specially developed for GB characterization. It will (2) understand the impact of GBs’ structure and stoichiometry on the electronic properties down to the nanoscale and (3) utilize the experimental evidence and the concepts gained to obtain high-performance photovoltaic and electronic devices. Finally, ProGB will employ a unique infrastructure that will be implemented and established in the PI’s team. It will apply atom probe tomography as a principal technique to determine the composition of GBs in Si (as a typical prototype material) and in AgBiS2 (as a novel material for ultrathin-film solar cell applications), combined with techniques to determine the materials’ electronic and structural properties on exactly the same position on the sample. In this way, ProGB can realize a true 1:1:1 correlation with unprecedented precision in material characterization. These experimental observations will be consolidated by theoretical simulations and device modeling. Last but not least, the outcome of ProGB will rescue thin-film technology with polycrystalline absorbers and will be of big benefit to the latest technologies such as tandem and ultrathin-film solar cells.
Projet ERC : MAGNETALLIEN (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed Matter Physics
Entité participante : Centre national de la recherche scientifique — Pays : France — Rôle : PI
Acronyme de l'entité : CNRS
Type d'entité : Recherche
Financement ERC de l'entité : 1844830.0 €

Limiting power consumption for massive data while increasing data processing speed and transfer rates requires the development of innovative architectures for logic, memory, and hyper frequencies applications. Spintronics brings some answers proposing solutions based on spin-orbit coupling. However, there is still a need to further reduce power consumption, simplify memory architectures, and close the THz gap. I propose unconventional experiments to develop magnetic-based platforms towards sub-THz spin current generation without the use of laser sources and gain access to the study of magnetic materials with high resonance frequencies. I formulate here new challenges presenting cutting-edge concepts involving spin-orbit coupling in magnetic alloys and compounds with magnetic Rashba interfaces. These will allow exploring and exploiting the ultra-high harmonics generation of spin currents, spin-orbitronics signals, in the sub-THz domain by tuning the ferromagnetic exchange and Rashba splitting. Based on these innovative concepts, MAGNETALLIEN has a twofold objective: i) to demonstrate that magnetic alloys and compounds, such as amorphous ferrimagnets and magnetic epitaxial Heusler, that possess strong spin-orbit coupling will lead to a new platform for efficient spin current self-production and, ii) will lead to the production of ultra-high harmonics spin pumping voltage up to tens or hundreds of GHz enabling various uses in new spin-orbitronics experiments. MAGNETALLIEN project will generate knowledge in spin current self-production and self-torque in magnetic alloys and compounds that will allow making decisive progress for less-energy-consuming architectures. Although of fundamental nature, demonstrating the existence of ultra-high harmonics signal which exploitation in innovative devices for spin-orbitronics goes far beyond the state of the art, this project will open new avenues and perspectives to tackle in a disruptive way all-electric sub-THz domain.
Projet ERC : CONUREP (Horizon Europe, appel 2025, Consolidator grants)
Entité participante : Université de Durham — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1981393.0 €

Understanding the unitary representations of a given group G is one of the most persistent problems of mathematics. If G is the integers, the ensuing theory is that of the Fourier transform. If G is the Heisenberg group, then the resulting representation theory is the theory of matrix models of Quantum Mechanics. Accelerating, if G is the absolute Galois group of the rationals, the theory is described by the Langlands program. My goal is to understand the finite dimensional (f.d.) unitary representations of discrete groups through the lens of how they can converge to the regular representation. I describe both weak and strong forms of convergence and focus mainly on strong convergence. I first ask which groups have f.d. unitary representations that strongly converge to their regular representation? What if we require representations to factor through permutation groups? These questions are deep, wide-ranging, and push far beyond the state-of-the-art. Next I ask to what extent strong convergence of f.d. unitary representations is generic, when we have a way to randomize representations. In particular this applies to the fundamental groups of closed surfaces, which are a test bed for the current program. In many cases randomization is the only tool we know to establish strong convergence, so we have as yet no explicit examples of the phenomenon that the proposal in centered on! We present an algebraic candidate that is intimately related to Selberg’s Eigenvalue Conjecture in automorphic forms. Most of the above questions have spectacular consequences to spectral gaps of locally symmetric spaces, a connection that I discovered with Hide. We do not understand this outside special cases yet. I ambitiously aim to completely describe the connections between strong convergence of representations of a lattice and their induced representations of the ambient Lie group. Finally, we imagine what lies beyond strong convergence and whether random matrix theory can take us there.

Mots-clés : unitary representation,spectral gap,discrete groups,c algebras
Projet ERC : GE4SPDE (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : Université de Münster — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : Universität Münster
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1948233.0 €

The project is concerned with the global behaviour of solutions to Stochastic Partial Differential Equations (SPDEs) from Mathematical Physics which arise e.g. in the description of scaling limits of interacting particle systems and in the analysis of Quantum Field Theories. The equations contain noise terms which describe random fluctuations and act on all length scales. In this situation the presence of a non-linear term can lead to divergencies. A subtle renormalisation procedure, which amounts to removing infinite terms, is needed. Over the last years the understanding of non-linear SPDEs has been revolutionised and a systematic treatment of the renormalisation procedure has been achieved. This led to a short-time well-posedness theory on compact domains for a large class of highly relevant semi-linear SPDEs. In this project, I will describe the global - both in time and over infinite domains - behaviour of solutions of some of the most prominent examples, by combining PDE techniques for the non-linear equations without noise and the improved understanding of the subtle small-scale stochastic cancellations. I have already pioneered such a programme in an important special case, the dynamic Phi-4 model. The project has three specific strands: A) Proving estimates for the stochastic quantisation equations of the Sine-Gordon and Liouville Quantum Gravity models and eventually Gauge theories, and to giving a PDE-based approach to the celebrated 1-2-3 scaling of the KPZ equation, B) giving PDE-based constructions of Phi-4 models in fractional dimension and describing phase transitions in terms of mixing properties of the dynamics, C) treating degenerate parabolic equations and exploring if systems that fail to satisfy a fundamental "sub-criticality" scaling assumption can still be treated using SPDE techniques.

Mots-clés : stochastic partial differential equations,regularity structures,a priori estimates,phase transitions,stochastic quantisation
Projet ERC : VORTEX (Horizon Europe, appel 2025, Consolidator grants)
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 : 1993750.0 €

The solar system's giant planets, Jupiter and Saturn, host atmospheres with clear belts, cloudy bands, and storms like the Great Red Spot. They represent the class of cool, isolated giant planets. For a full view of giant planet atmospheres we must study the known exoplanet population over a wide range of orbits, temperatures, and spins. However, exoplanets are too far away to resolve them directly.  The James Webb Space Telescope is changing this: time-resolved spectra probe the atmospheres of rotating wide-orbit planets, whose light curves reveal active weather. The Extremely Large Telescope's METIS and ANDES instruments will study giant planets from close to wide orbits at high spectral resolution, probing winds through Doppler shifts, allowing us to constrain flow and temperature structures.  In spite of these amazing prospects, there is no framework to properly fit JWST or future ELT data. State-of-the-art techniques are prohibitively slow, preventing hypothesis testing. This is a fundamental challenge that needs to be resolved - otherwise we cannot use the information encoded in our data.  With the VORTEX project I will develop the machinery to map the 3D, dynamical atmospheres of giant exoplanets, from strongly irradiated to the most isolated ones. The modeling challenge will be addressed through a feedback between novel machine learning inference methods and hydrodynamical models. Our project will answer the three questions: (i) what are the properties, scales and lifetimes of storms on isolated planets? (ii) how do flows transport gas on irradiated planets? (iii) how do atmospheres of planets on eccentric orbits react to changing irradiation?  VORTEX will have a long-lasting impact by enabling the 3D interpretation of exoplanet spectra. Answers to the questions above will reveal which processes govern the 3D atmospheres of exoplanets. Our methods will pave the way for analyzing similarly complex astronomical datasets recorded with the largest telescopes.

Mots-clés : 3d atmospheric characterization,machine learning inference
Projet ERC : PARIS (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Université de Stockholm — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999444.0 €

CO2 capture, storage and utilization is judged critical to mitigate the rapid rise in the atmospheric CO2 concentration. A key problem is the gigantic mass of CO2 emitted, which asks for robust, efficient and economically viable approaches that are currently missing and limited by the lack of suitable materials. To break through this barrier, I aim to develop metal-free dual-function porous poly(ionic liquid)s (DPPs) to capture and convert CO2 under ambient conditions into cyclic carbonates with high efficiency, and to apply them in model reactors for cost-effective processing of CO2.  Poly(ionic liquid)s (PILs) are innovative ionic materials, in which ionic liquids (ILs) are covalently joined by a macromolecular backbone. ILs are known CO2-philes, and IL-derived PILs are naturally in favour of CO2 sorption, while their ions can be tailor-made for catalytic CO2 transformation. Such dual-function as sorbent and catalyst is the intrinsic merit of PILs to address the CO2 challenge, but unfortunately has been long impeded by the mismatched chemical structures in each function. Our preliminary work proved that the newly emerging 1,2,4-triazolium PILs were catalytic active and drastically more CO2-philic than common polyimidazoliums, and are believed as the game-changer materials. We envision that by structuring chemically tailor-made 1,2,4-triazolium PILs into highly porous materials, they will be able to capture and convert CO2 under ambient conditions. This ground-breaking materials concept will circumvent the complicated, harsh conditions for CO2 fixation, and cut the cost to an affordably low level.  This project will radically advance scientific knowledge and technology to fixate and convert CO2 at scale into value-added chemicals that further reduces the consumption of fossil resources. Its outcome will expedite the research in PIL and dual-function materials to revolutionize the CCU routes and equip us with powerful materials tools to mitigate the global CO2 rise.

Mots-clés : poly ionic liquid,co2 capture and utilization
Projet ERC : Counterfact (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
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 : 1997500.0 €

Reasoning about what might have been, about alternatives to our own pasts, is a landmark of human intelligence. Such type of reasoning, called counterfactual reasoning, is often evaluative, specifying alternatives that are in some sense better or worse than our past reality, and has been shown to play a significant role in the ability that humans have to learn from limited past experience and improve their decision making skills over time. In recent years, there has been an increasing excitement on the potential of machine learning models and algorithms to support human decision making in a variety of high-stakes domains such as medicine, education or science. However, these models and algorithms have been traditionally unable to perform, nor benefit from, counterfactual reasoning. In this project, our goal is to bridge this gap.  We will develop machine learning models and algorithms for automated decision support that are able to perform and benefit from counterfactual reasoning in multiple ways. For example, they will perform counterfactual reasoning about human behavior to anticipate how humans incorporate algorithmic advice into their decisions. This will enable a new generation of decision support systems that can only increase and never decrease the average quality of human decisions. Moreover, they will use the structural similarities and shared properties across different counterfactual decision making scenarios to significantly reduce their computational and data requirements. In addition, these models and algorithms will also help humans learn from their own past decisions by identifying alternative decisions that would have led to better outcomes. Finally, we will perform large-scale human subject studies with both laypersons and experts to evaluate their effectiveness in a wide variety of decision making tasks.

Mots-clés : human aspects of machine learning,human ai collaboration,decision support systems,counterfactual reasoning,causality,uncertainty quantification
Projet ERC : BYTE2BITE (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems E ngineering
Entité participante : Société Max-Planck pour le développement des sciences — Pays : Allemagne — Rôle : Partner
Acronyme de l'entité : Société Max Planck
Type d'entité : Recherche
Financement ERC de l'entité : 887862.5 €

Potato is one of the three most important food crops in the world. Around 1.3 billion people rely on potato as a staple food every day. But despite this exceptional socio-economic importance, the improvement of potato during the past 150 years has been limited. The main reason for this deficit is the highly heterozygous, autotetraploid genome of potato. This complicates several important aspects of breeding including the fixation of beneficial alleles and the implementation of modern, genomics-assisted breeding techniques. Recently it has been suggested to convert tetraploid potato into a diploid crop, and thereby overcome all the difficulties associated with tetraploid potato breeding. However, the creation of diploid potatoes is hampered by the large number of deleterious recessive mutations that have accumulated in the tetraploid genome and are exposed in the diploid potatoes. In BYTE2BITE we will address the fundamental issues that hold back the success of potato breeding. We will generate the first-of-its-kind, near-to-complete pan-genome of potato and use this resource to develop unprecedented genome-graph tools for efficient genotyping and haplotype-resolved genome analysis. We will then use this new computational toolbox to establish a genomics-assisted pre-breeding programme in which we will generate novel potato cultivars with low mutational load. This new resource will open the doors to efficient breeding unleashing the genetic potential of potato and thereby help to secure the way we feed the world for the decades to come.  Specifically, in BYTE2BITE we want to achieve the following goals: 1. A near-to-complete pan-genome describing almost the entire haplotype diversity of potato 2. A genome graph tool for cost-efficient genotyping and haplotype-resolved genome analyses 3. A genomics-assisted pre-breeding programme for potatoes with low mutational load

Mots-clés : pan genomics,plant genome assembly,potato breeding