Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : MiCoS (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems E ngineering
Entité participante : Instituut voor Landbouw en Visserijonderzoek — Pays : Belgique — Rôle : Partner
Acronyme de l'entité : ILVO
Type d'entité : Recherche
Financement ERC de l'entité : 177625.0 €

Plastics are found in nearly every environment and disrupt key ecosystem services. While the effect of plastic in marine and freshwater ecosystems has been studied extensively, effects of plastic on soil ecosystem functions such as plant growth, microbial biomass and water permeability have been mostly overlooked, particularly for the smallest particles, the microplastics (MPs; ≤ 5 mm). The lack of appropriate techniques and methodologies for sampling, extraction and detection hamper the research concerning MP distribution in soil ecosystems. This research gap has led to fragmentary knowledge and even contradictory results in MP studies on soil ecosystems, as different concentrations, sizes, shapes and MP polymer types were considered. Therefore fundamental insights in the role of MPs on soil and plant health are missing. We will advance the field by working in a three step approach to mechanistically define how MP pollution outbalances the soil (and plant) ecosystem. First, the risk of MP pollution in soil will be assessed by correlating MP concentrations of 240 soils with soil health indicators. To measure the MP concentration, a fast, cost-effective and standardized method to detect, identify and quantify MPs (≥ 1 µm) in soils will be developed. Second, these correlations between MPs and the soil health indicators will be validated and tested in greenhouse experiments, to understand the biological underpinnings that drive these correlations. MP induced changes in plant growth, plant disease susceptibility, soil texture, soil chemical composition and the microbial community will be studied. Third, to reduce the risk (introduction and accumulation) of MPs in soil ecosystems, we will actively search for biodegrading organisms making use of a novel sequencing approach. With this multidisciplinary approach, we will be able to mechanistically define the effects of MPs on soil and plant health and advance the field by identifying plastic-biodegrading microorganisms.

Mots-clés : microplastics,soil health,plant development,dna stable isotope probing,statistical modelling
Projet ERC : HighLEAP (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : École nationale des ponts et chaussées — Pays : France — Rôle : PI
Acronyme de l'entité : ENPC
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1379858.0 €

Interacting particle- or agent-based systems are ubiquitous in science. They arise in an extremely wide variety of applications including materials science, biology, economics and social sciences. Several mathematical models exist to account for the evolution of such systems at different scales, among which stand stochastic differential equations, optimal transport problems, Fokker-Planck equations or mean-field games systems. However, all of them suffer from severe limitations when it comes to the simulation of high-dimensional problems, the high-dimensionality character coming either from the large number of particles or agents in the system, the high amount of features of each agent or particle or the huge quantity of parameters entering the model.  The objective of this project is to provide a new mathematical framework for the development and analysis of efficient and accurate numerical methods for the simulation of high-dimensional particle or agent systems, stemming from applications in materials science and stochastic game theory. The main challenges which will be addressed in this project are: -sparse optimization problems for multi-marginal optimal transport problems, using moment constraints; -numerical resolution of high-dimensional partial differential equations, with stochastic iterative algorithms; -efficient approximation of parametric stochastic differential equations, by means of reduced-order modeling approaches.  The potential impacts of the project are huge: making possible such extreme-scale simulations will enable to gain precious insights on the predictive power of agent- or particle-based models, with applications in various fields, such as quantum chemistry, molecular dynamics, crowd motion or urban traffic.

Mots-clés : optimal transport,quantum chemistry,tensor methods,reduced order modeling,molecular dynamics,agent based models,high dimensional pdes,mean field games,greedy algorithms
Projet ERC : QFT.zip (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental Constituents of Matter
Entité participante : École Nationale Supérieure des Mines de Paris — Pays : France — Rôle : Partner
Acronyme de l'entité : Mines Paris - PSL
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 288750.0 €

Many-body quantum systems with strong correlations are particularly difficult to understand in the continuum, where non-perturbative techniques are in scarce supply. Direct diagonalization methods are not available, since the Hilbert space is simply too large to be manageable. This inhibits progress in high energy physics, nuclear physics, and in the study of exotic topological phases of matter. On the lattice, tensor network states, a variational class of wavefunctions coming from quantum information theory, have allowed to compress exponentially large Hilbert spaces down to a smaller numerically manageable corner. This has allowed substantial theoretical and numerical advance on the many-body problem on the lattice.  This project will develop continuous tensor network states, a new framework to extend the recent lattice progress to the continuum and quantum field theory (QFT). The originality of the approach is that it will not rely on any discretization of space-time. I will work directly in the continuum, without any cutoff. Low energy states of quantum field theories, which a priori live in a continuously infinite dimensional Hilbert space, will be compressed down to a finite and small number of parameters. This will then allow to solve numerically very generic (non-integrable) strongly coupled theories in a fully non-perturbative manner. Such a compression was long thought to be impossible, in particular in the relativistic case, but I overcame crucial theoretical hurdles in the past year, making the proposal particularly timely.  I will construct this framework with 3 main applications in mind: i) non-relativistic problems in 2 space dimensions and more, including e.g. fractional quantum Hall states, ii) relativistic QFT, starting with 1+1 dimensional toy model and gradually increasing complexity to get closer to nonabelian gauge theories, iii) critical quantum systems (and classical statistical mechanics).

Mots-clés : tensor networks,quantum field theory,variational wave functions,entanglement
Projet ERC : CRISP (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and Communication Engineering
Entité participante : École Nationale Supérieure des Mines de Paris — Pays : France — Rôle : Partner
Acronyme de l'entité : Mines Paris - PSL
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 314925.0 €

Can we turn the Energy Crisis into an opportunity that advances the clean energy transition? Yes, we can, and we pave the path leading to just and equitable decarbonization. Debates that followed extreme events in Europe and the US pointed to a “wind of change” for the power market design and the price discovery process. Operators are put in the corner, inheriting market schedules that they more and more amend using their judgement, whereas they are also accountable for their out-of-market decisions. The process is inherently sub-optimal and costly! Extreme events may turn annual market revenues into daily energy costs! I will explore innovative ideas in Generalized Linear Programming and adaptive robust optimization with data-driven uncertainty sets, to support network and reliability related operator decisions, and novel machine learning developments to remove computational barriers while maintaining interpretability. Tackling major obstacles that derive from non-convexities, externalities, and uncertainty (to date open problems), leveraging demand adaptability, and combining optimization with economic theory, I will break new ground by (i) defining efficient, adaptive, and robust prices that minimize the gap between market outcomes and operator decisions, and (ii) designing innovative protection mechanisms that dynamically delineate the (blurred) boundaries between systems and markets encoding crisis resilience and shielding from catastrophic financial impacts. CRISP will break the barriers that have restricted power markets to “half-markets” for more than 30 years tapping adaptable demand and set the foundations for efficient scheduling and correct investment signals in innovation, infrastructure, resource adequacy, and sustainability. The theoretical and computational breakthroughs will benefit a wide class of problems (scheduling, routing, and others) and are also relevant to integrated grids and systems hampered similarly by climate change and extreme events.

Mots-clés : power systems markets,price discovery,non convexities,externalities,uncertainty,adaptable demand,generalized linear programming,interpretable machine learning,optical classification trees
Projet ERC : NUQNET (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental Constituents of Matter
Entité participante : Conseil supérieur de la recherche scientifique — Pays : Espagne — Rôle : Partner
Acronyme de l'entité : CSIC
Type d'entité : Recherche
Financement ERC de l'entité : 520968.35 €

Neutrino oscillation experiments are entering a new era of precision, utilizing cutting-edge technologies and capabilities to offer unprecedented insights The ERC project NUQNET aims to enhance the prediction accuracy of neutrino-nucleus interactions by implementing advanced machine learning techniques. This endeavor involves incorporating predictive errors, a critical step necessary to meet the requirements and fully unlock the discovery potential of upcoming neutrino oscillation experiments. Presently, there is no theoretical method capable of consistently describing neutrino interactions across the wide energy spectrum investigated in neutrino experiments. Moreover, existing models in the market rely either on some approximations or on semi-phenomenological approaches, making it challenging to rigorously assess the theoretical uncertainty. This uncertainty must be meticulously propagated throughout the analysis to extract precise oscillation parameters. The PI plans to go beyond the limitations of traditional many-body techniques by using ANN architectures to represent the nuclear wave functions and obtain the spectral function of several nuclei relevant for oscillation experiments (see next section for details). A novel methodology to incorporate quantum-mechanical effects into the description of final state interactions, a domain typically modeled by semi-classical intra-nuclear cascades in neutrino event generators, will be introduced. To achieve this goal, the PI will pioneer the development of a new real-time Variational Monte Carlo algorithm, building upon the formalism employed to obtain the ANN nuclear-wave functions.  New tools will be developed to describe the high-energy region relevant for DUNE in which the degrees of freedom switch from nucleons and pions to partons. Finally, the PI will coordinate a pioneering effort to estimate the uncertainty of the theoretical predictions needed for oscillation analysis and to be able to discover new physics.

Mots-clés : neutrino nucleus cross sections electroweak interactions uncertainty quantification in neutrino oscillation experiments nuclear structure and dynamics artificial neural network quantum states real time dynamics
Projet ERC : SoftEn (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : Université de Maastricht — Pays : Pays-Bas — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

This project will establish soft enforcement of EU migration law as a new research field in which legal and political science scholars will be equally active. Migration is a salient issue for the EU, and we are witnessing deep shifts in the implementation and enforcement landscapes of EU migration law. Traditional enforcement methods, such as Court of Justice proceedings initiated by the European Commission against Member States, have met their limit. Soft enforcement is becoming a flexible and politically palatable alternative. For example, Fundamental Rights Offices have been established within EU migration agencies that operationalise novel Ombuds-type administrative accountability processes, and a peer review mechanism, run jointly by the Commission and Member States, has been integrated with the Schengen area of free movement. SoftEn conceptualizes soft enforcement and unlocks the black box of its operationalisation in the context of migration. It brings together theoretical insights from legal and political science on governance, enforcement, and accountability, combining legal analysis with empirical qualitative methods. Its groundbreaking nature lies in three characteristics. First, the project will generate a new interdisciplinary, legally grounded and empirically informed theory of soft enforcement in the migration context. Second, it will generate datasets on soft enforcement along three axes (steering policy implementation; supervising policy implementation; and ensuring actor accountability) across the EU’s migration policies. Moving beyond ‘law on the books’, it will also generate unique qualitative empirical insights at EU and selected national levels. Third, it will generate a new evidence-informed governance framework for soft enforcement that can impact the work of public institutions at EU and national levels, and ultimately, individuals.

Mots-clés : european union eu law eu migration law eu asylum law eu law enforcement fundamental rights administrative governance multi level governance accountability
Projet ERC : FOOD CHARITIES (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Soci al World and Its Interactions
Entité participante : Université de Maastricht — Pays : Pays-Bas — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499144.0 €

Food charities have become essential pillars in assisting the urban poor, offering a crucial buffer between food insecurity and hunger. Their significance, particularly after the Great Recession, is underscored by two pivotal shifts: one towards the sustainable utilisation of food surplus and waste reduction, and the other reflecting societal changes with diminishing welfare benefit provision. While considerable research addresses food insecurity and support, the literature lacks a robust framework to understand and compare Charitable Food Provision (CFP) across countries and cities. Despite the almost universal occurrence of such transitions, existing studies have not yet contextualised this phenomenon within the diverse arrangements of welfare and civil society regimes, and the influence of transitions in agri-food sustainability on CFP. Accordingly, this proposal aims to: 1. Use event sequence analysis to chart the historical trajectory of CFP in Italy, Japan, and the Netherlands, examining the emergence and consolidation of professionalised CFP sectors in diverse institutional settings. 2. Adopt mixed-methods social network analysis to examine current CFP dynamics in Palermo, Kyoto, and Rotterdam, focusing on providers interrelationships, operational modes, and their ties with both state and non-state authorities. 3. Harness urban ethnography to investigate the interdependence between food insecurity and support to understand how CFP becomes part of poor people’s survival strategies. Additionally, it introduces an innovative tool, tailored for city workshops, to facilitate real-time data collection on (CFP) urban dynamics. Hence, it combines the findings obtained by crafting an overarching framework to extend CFP comparison to other countries and cities. The research will create new knowledge across urban, sustainability, organisation, food, and social policy studies, offering an innovative, comparative insight into food support and insecurity.

Mots-clés : ethnography field theory food insecurity food support mixed methods social network analysis urban poverty
Projet ERC : NextGeneTx (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Telethon Foundation — Pays : Italie — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 1400000.0 €

Despite 6 products on the market and dozens in advanced phases of clinical development, in vivo gene therapy with vectors derived from adeno-associated viruses (AAV) still faces the major challenge of the limited vector cargo capacity (< 5kb). This prevents AAV application for treatment of inherited diseases caused by mutations in large genes. To address this, we have recently developed an AAV-based strategy relying on the use of short protein elements called inteins. These, when fused at the extremities of fragments of a large protein delivered to cells through AAV, can mediate joining of the fragments in a traceless manner, resulting in the reconstitution of the intact target protein. However, despite being extensively utilized as biotechnological tools in vitro, the in vivo application of inteins for therapeutic purposes comes with several limitations. These include: insufficient levels of full-length protein reconstitution for some applications; complex design; and production of undesired, potentially harmful by-products. NextGeneTx aims to expand the potential of AAV-inteins as an innovative in vivo biotechnological tool for therapeutic purposes, by addressing each of these limitations. To achieve this, we will leverage cutting-edge technologies to: i. engineer inteins with improved efficiency and greater design flexibility; ii. modulate intracellular trafficking of intein-containing protein fragments to enhance the efficiency and accuracy of full-length protein reconstitution; iii. develop strategies to reduce the production of unwanted by-products. We will test the therapeutic relevance of evolved AAV-inteins in both gene therapy and genome editing applications, using human retinal organoids and animal models of retinal diseases. The outcomes of NextGeneTx will define a highly adaptable, safe, and effective next-generation AAV-based approach to deliver large genes, thus significantly expanding the patient population that can benefit from in vivo gene therapy.

Mots-clés : aav,gene therapy,genome editing,large gene delivery,retinal organoids,animal models,inherited retinal diseases,directed evolution
Projet ERC : RobustNanoNet (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and Communication Engineering
Entité participante : National Institute for Research and Development in Microtechnologies — Pays : Roumanie — Rôle : PI
Acronyme de l'entité : IMT
Type d'entité : Recherche
Financement ERC de l'entité : 2446250.0 €

Resistive switching refers to the controlled change in resistance of an electronic material, e.g. metal oxide, via the creation and modulation of nanoscale filaments. Although its physics is not yet fully understood, resistive switching devices (called memristors) are promising as efficient artificial synapses in neuro-inspired computing systems. However practical challenges exist. Current devices excel in only a few of the performance metrics necessary for circuit and system integration. Moreover, they exhibit non-idealities causing neuromorphic systems using these devices to have low performance. The project will address this key issue by pursuing device-system co-optimization across four objectives, aiming to engineer a single “hero” resistive switching technology with all the desired metrics. Aim 1 will develop resistive switching devices based on a new class of materials with broad compositional space, called high entropy oxides. Promising compositions will be fabricated in a high throughput fashion. In Aim 2, a proposed characterization method via a state-of-the-art mid-infrared laser will help understand in-operando the filamentary switching at nanoscale and uncover the physical mechanisms behind its non-idealities. The fabrication and characterization will iteratively target a broad range of performance metrics. Some metrics can only be quantified across a population of devices, so Aim 3 will integrate the optimized devices on transistor circuitry for benchmarking at scale. Aim 4 targets the applicability of these devices to next generation neuromorphic systems for machine learning training. Preliminary work on a multi-layer neural network validated this concept and indicated the need for co-optimization, as proposed. RobustNanoNet will address the interdisciplinary challenges towards a reliable resistive switching technology to support robust neuromorphic systems for energy efficient computing.

Mots-clés : memristor,resistive switching,neuro inspired,neuromorphic,hardware mappable neural networks,machine learning accelerators
Projet ERC : AGATHA CRYPTY (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
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 : 1448540.0 €

Contemporary public-key cryptography builds its foundations on a handful of computational problems rooted in arithmetic and geometry. The vast majority of deployed cryptosystems rely on two classical problems (computing discrete logarithms, and factoring integers) that would not resist a large-scale quantum computer. Research on quantum technology is accelerating, endangering the world's information systems. New foundations are being proposed by the cryptologic community, promising post-quantum security, but suffering in many aspects from the lack of adequate scrutiny.  Emerging post-quantum candidates can be naturally embedded into rich and modern mathematical theories. It is the case of lattice-based and isogeny-based cryptography, which share surprising connections once recast in the world of algebraic groups. Algebraic groups are at the forefront of modern mathematics. Their study across the past century has blossomed with the development of powerful theories, such as representation theory and automorphic forms. Yet, the dialogue between arithmeticians and cryptologists has been sparse, and the link between algebraic groups and the objects of post-quantum cryptography has been mostly anecdotal.  This project brings this connection to the forefront, observing that the theory of algebraic groups shines a powerful light on problems raised by lattice-based and isogeny-based cryptography. It has the unique ability to turn the set of all instances of a computational problem into one meaningful object in itself — a 'moduli space' — with an arithmetic structure, a geometry, a topology, a harmonic theory. Exposing these problems to the powerful artillery of modern arithmetic will lead to cryptanalytic breakthroughs, security proofs, and the construction of cutting-edge cryptosystems.

Mots-clés : post quantum cryptography,computational number theory