Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : META-LEGO (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : Institut national des sciences appliquées de Lyon — Pays : France — Rôle : Partner
Acronyme de l'entité : INSA de Lyon
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 0.0 €

META-LEGO will bring the knowledge needed to design metamaterials/classical-materials structures that control elastic waves and recover energy. For this, I will develop, implement and validate a new paradigm for finite-size metamaterials’ modeling, by leveraging the relaxed-micromorphic model that I have contributed to pioneer. The presence of boundaries in metamaterials strongly affects their response when coming in contact with mechanical loads. Yet, we still lack an exhaustive model to predict the static/dynamic response of finite-size metamaterials: current homogenization methods are unsuitable to provide a coherent transition from infinite- to finite-size metamaterials modeling. This prevents us from exploring realistic structures combining metamaterials’ and classical-materials’ bricks of finite size. META-LEGO hypothesizes that the mechanical response of finite-size metamaterials can be explored going beyond classical homogenization. Instead, I will create an elastic- and inertia-augmented micromorphic model with embedded internal lengths to describe the main metamaterials’ fingerprint characteristics, such as anisotropy, dispersion, band-gaps, size-effects, etc. To provide this paradigm shift, I will focus on 4 objectives: 1. Model metamaterials’ response under static/dynamic loads 2. Implement the model on infinite-size metamaterials 3. Validate the model on finite-size metamaterials 4. Design and manufacture metamaterials/classical-materials structures able to control elastic waves and recover energy The reduced model’s structure (free of unnecessary parameters), coupled with well-posed boundary conditions, will allow us to unveil the static/dynamic response of both real and not-yet-existing metamaterials’ bricks of arbitrary size and shape. Playing LEGO with such bricks, we will be able to design and optimize surprising meta-structures, such as noise- and vibration-controlled railway stations, or meta-cities entirely protected from seismic waves.

Mots-clés : enriched continuum models,micromorphic models,mechanical metamaterials,heterogeneities,finite size metamaterials,large scale meta structures
Projet ERC : MONACAT (Horizon 2020, appel 2015, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Université de Toulouse — Pays : France — Rôle : Partner
Acronyme de l'entité : UT
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 48025.0 €

MONACAT proposes a novel approach to address the challenge of intermittent energy storage. Specifically, the purpose is to conceive and synthesize novel complex nano-objects displaying both physical and chemical properties that enable catalytic transformations with a fast and optimum energy conversion. It follows over 20 years of research on “organometallic nanoparticles”, an approach of nanoparticles (NPs) synthesis where the first goal is to control the surface of the particles as in molecular organometallic species. Two families of NPs will be studied: 1) magnetic NPs that can be heated by excitation with an alternating magnetic field and 2) plasmonic NPs that absorb visible light and transform it into heat. In all cases, deposition of additional materials as islands or thin layers will improve the NPs catalytic activity. Iron carbides NPs have recently been shown to heat efficiently upon magnetic excitation and to catalyse CO hydrogenation into hydrocarbons. In order to transform this observation into a viable process, MONACAT will address the following challenges: determination and control of surface temperature using fluorophores or quantum dots, optimization of heating capacity (size, anisotropy of the material, crystallinity, phases: FeCo, FeNi, chemical order), optimization of catalytic properties (islands vs core-shell structures; Ru, Ni for methane, Cu/Zn for methanol), stability and optimization of energy efficiency. A similar approach will be used for direct light conversion using as first proofs of concept Au or Ag NPs coated with Ru. Catalytic tests will be performed on two heterogeneous reactions after deposition of the NPs onto a support: CO2 hydrogenation into methane and methanol synthesis. In addition, the potential of catalysis making use of self-heated and magnetically recoverable NPs will be studied in solution (reduction of arenes or oxygenated functions, hydrogenation and hydrogenolysis of biomass platform molecules, Fischer-Tropsch).

Mots-clés : organometallic chemistry
Projet ERC : FUN2MODEL (Horizon 2020, appel 2018, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Birmingham — Pays : Royaume-Uni — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 41653.64 €

Machine learning is revolutionising computer science and AI. Much of its success is due to deep neural networks, which have demonstrated outstanding performance in perception tasks such as image classification. Solutions based on deep learning are now being deployed in real-world systems, from virtual personal assistants to self-driving cars. Unfortunately, the black-box nature and instability of deep neural networks is raising concerns about the readiness of this technology. Efforts to address robustness of deep learning are emerging, but are limited to simple properties and function-based perception tasks that learn data associations. While perception is an essential feature of an artificial agent, achieving beneficial collaboration between human and artificial agents requires models of autonomy, inference, decision making, control and coordination that significantly go beyond perception. To address this challenge, this project will capitalise on recent breakthroughs by the PI and develop a model-based, probabilistic reasoning framework for autonomous agents with cognitive aspects, which supports reasoning about their decisions, agent interactions and inferences that capture cognitive information, in presence of uncertainty and partial observability. The objectives are to develop novel probabilistic verification and synthesis techniques to guarantee safety, robustness and fairness for complex decisions based on machine learning, formulate a comprehensive, compositional game-based modelling framework for reasoning about systems of autonomous agents and their interactions, and evaluate the techniques on a variety of case studies. Addressing these challenges will require a fundamental shift towards Bayesian methods, and development of new, scalable, techniques, which differ from conventional probabilistic verification. If successful, the project will result in major advances in the quest towards provably robust and beneficial AI.

Mots-clés : deep learning,multi agent systems,probabilistic verification,bayesian inference,temporal logic,stochastic games,equilibria
Projet ERC : Macro Identification (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : University of Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1312383.0 €

Macroeconomic data are largely non-experimental. Thus, causal inference in macroeconomics is largely based on assumptions about what aspects of the variation in the data are exogenous. This presents two major challenges, which this research addresses directly. First, few such assumptions are generally accepted. Second, conditional on any set of assumptions, identification of causal effects is often weak because there is little relevant variation in the data. To tackle these challenges, I propose three lines of enquiry to explore new sources of identification and develop the requisite econometric methods. The first line will study the implications of the so-called ‘zero lower bound’ (ZLB) on nominal interest rates for identification. The key novel insight is that the ZLB causes monetary policy to be set at least in part exogenously. This can be thought of as a natural experiment that generates a new instrument to identify the underlying policy model. This insight applies more generally to policy functions subject to exogenous constraints. The informativeness of these constraints depends on the probability that they bind, so recent experience makes the ZLB a promising application of the idea. The second line will analyse new ways of using time-variation in some of the parameters of macroeconomic models, such as trend inflation or the volatility of shocks, to study important open questions in macro, such as the degree of forward versus backward-looking behaviour and the ‘good luck versus good policy’ debate. The third line will contribute to the on-going research on developing methods of inference that are robust to weak identification. This is a pervasive problem in macro that threatens the validity of structural inference under any identification scheme. The synergies among these three lines' methodological analyses will accelerate progress on each line well beyond what would be possible in a piecemeal approach.

Mots-clés : econometric theory,instrumental variable methods,weak instruments
Projet ERC : MACROUNCERTINEQ (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : University of London — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : U. London
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 957089.0 €

Macroeconomic policies and macroeconomic institutions influence aggregate outcomes along a number of significant dimensions. While the empirical literature has traditionally focussed on the direct effects on economic activity and inflation, little is known on the impact that policy and institutional changes exert on the macroeconomy through their indirect effects on the distribution of resources available to households and firms. This proposal describes my research agenda over the next five years to fill this important gap in academic and policy knowledge. The emphasis is on a new empirical framework to revisit the transmission mechanism of changes in macroeconomic policies and institutions through their (possibly unintended) consequences on uncertainty, risk and inequality across diverse groups of society and across countries. The proposed approach combines survey data, international evidence and a narrative identification of policy and institutional changes from the analysis of historical records. Another main contribution will be the development of analytical frameworks to account for the stylized facts uncovered by the proposed empirical approach. These include models of imperfect information on individual tax rates and inter-generational risk-sharing within households as well as characterizations of the way monetary institutions and labour market regulations interact to affect macroeconomic uncertainty and financial market volatility. The ambition is to generate a set of testable predictions that could then be used to identify and assess the relative merits of specific theoretical mechanisms in the data. On the policy side, this research will provide new estimates for the aggregate effects of government and monetary interventions. Furthermore, it will make it possible to identify the groups who have benefitted/suffered most from the specific changes that have dominated most of the recent past and whose redistributive implications appear so far overlooked.
Projet ERC : FIREQUENCHER (Horizon 2020, appel 2019, Proof of concept grants)
Domaine scientifique : Life Science
Panel : Molecules of Life: Biological Mechanisms, Structures and Functions
Entité participante : FIRC Institute of Molecular Oncology — Pays : Italie — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 150000.0 €

Macrophages are innate immune cells that specialise in sensing and responding to the presence of pathogens and they have been causally involved in the pathogenesis of both sepsis and rheumatoid arthritis (RA). Both diseases represent a high cost to the economy and society of European countries characterized by an aging population as well as developing countries. Sepsis is the first cause of death in hospitalized patient, with an estimate of 5 million deaths each year. Currently, there are no effective treatment protocols or strategies to deal with sepsis and several approaches targeting known cellular pathways have failed. RA is a chronic condition that affects 1% of the population worldwide. Despite the existence of established guidelines, 66% of patient inadequately respond to the therapy. Our group discovered that DNA damage is involved in the control of macrophages activation, unveiling a novel and untapped pathway potentially ripe for new approaches to modulate their activity. Thus, our proposed project is to test drugs targeting factors involved in DNA metabolism to treat inflammatory immune disease. Our plan involves a first part using an in vitro strategy to test the efficacy of the inhibitors on different macrophages cell lines and a second part involving the use of well-established animal disease models.

Mots-clés : sepsis,rheumatoid arthritis,inflammation response,dna repair,dna damage response,enhancer transcription
Projet ERC : MODERATE (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : Ludwig-Maximilians-Universität München — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : LMU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2821036.0 €

Magmatism is central to planetary evolution, controlling differentiation, volcanic eruptions and most geothermal systems. The presence of volatiles regulates magma buoyancy, storage and transport. As temperature, pressure and stress evolve – during transport, crystallisation, earthquakes or geothermal exploration – volatiles exsolve and either accumulate or escape magma, influencing the probability and style of an eruption. Understanding the controls on magma-gas coupling is thus fundamental to resolve volcanic activity as well as manipulate its response to drilling operations when harnessing magma energy. This project aims to constrain Magma outgassing during eruptions and geothermal exploration (MODERATE). I propose a timely, innovative, interdisciplinary project to radically broaden our understanding of magma-gas coupling in volcanic and (superhot) geothermal systems. The objectives are to: 1) constrain the impact of external forcing mechanisms (i.e., decompression, temperature changes, shear and pressure oscillations from shaking, impact and rupture) on the kinetics of vesiculation, 2) quantify the development of magma permeability, and 3) define thermo-mechanical strategies to moderate magma outgassing and alleviate the risk of volcanic eruption associated with drilling and borehole operations. A map of magma vesiculation and permeability under a wider range of conditions and stressing scenarios than ever achieved before will allow us to resolve current conundrums and explore new regimes to control permeability in magma. This innovative effort will allow us to moderate magma outgassing, thus enabling direct access to magma energy and eventually providing a means to control outgassing and alleviate eruption potential.
Projet ERC : NewWorlds (Horizon 2020, appel 2016, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : Université de Toulouse — Pays : France — Rôle : Partner
Acronyme de l'entité : UT
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 100000.0 €

Magnetic fields impact the formation of low-mass stars and their planets, and contribute to setting adequate conditions for life to appear. They control the amount of material and angular momentum from which stars and their planets form and mature, and can save newborn close-in planets from falling into their host stars. Magnetic fields can also affect planets by eroding their atmospheres and limiting their habitability, while hampering at the same time their detectability. Our understanding of these issues is however limited and critically needs observational guidance. To achieve a breakthrough on these forefront topics, we will exploit SPIRou, a state-of-the-art near-infrared spectropolarimeter / velocimeter integrated in our group and borne to become in 2017 the best instrument worldwide for such science. By pushing SPIRou to its ultimate performance in terms of sensitivity and precision, we will unveil magnetic fields and planets of young stars and nearby M dwarfs using our expertise in tomographic imaging and activity filtering. NewWorlds will in particular: - explore magnetic topologies of low-mass forming stars & accretion discs, study how they depend on stellar mass & age and how they control the angular momentum evolution of young stars; - discover newborn hot Jupiters around young stars, investigate their impact on the formation and evolution of planetary systems, and assess the role of magnetic fields in their survival; - detect and characterize planetary systems around nearby M dwarfs, focussing on terrestrial planets within the habitable zone to assess the impact of fields on their habitability. With this ERC grant, I will form a team dedicated to addressing these challenging goals in a comprehensive way. Ambitious, feasible and timely, NewWorlds will allow our team to optimally exploit SPIRou to unlock our understanding of how new worlds form, and will clear the path for future follow-up programmes with major facilities like the JWST and the E-ELT.

Mots-clés : stellar magnetic fields,low mass protostars,low mass stars,exoplanets,stellar surface imaging,spectropolarimetry,high precision velocimetry,high precision photometry
Projet ERC : NewWorlds (Horizon 2020, appel 2016, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
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 : 3387580.0 €

Magnetic fields impact the formation of low-mass stars and their planets, and contribute to setting adequate conditions for life to appear. They control the amount of material and angular momentum from which stars and their planets form and mature, and can save newborn close-in planets from falling into their host stars. Magnetic fields can also affect planets by eroding their atmospheres and limiting their habitability, while hampering at the same time their detectability. Our understanding of these issues is however limited and critically needs observational guidance. To achieve a breakthrough on these forefront topics, we will exploit SPIRou, a state-of-the-art near-infrared spectropolarimeter / velocimeter integrated in our group and borne to become in 2017 the best instrument worldwide for such science. By pushing SPIRou to its ultimate performance in terms of sensitivity and precision, we will unveil magnetic fields and planets of young stars and nearby M dwarfs using our expertise in tomographic imaging and activity filtering. NewWorlds will in particular: - explore magnetic topologies of low-mass forming stars & accretion discs, study how they depend on stellar mass & age and how they control the angular momentum evolution of young stars; - discover newborn hot Jupiters around young stars, investigate their impact on the formation and evolution of planetary systems, and assess the role of magnetic fields in their survival; - detect and characterize planetary systems around nearby M dwarfs, focussing on terrestrial planets within the habitable zone to assess the impact of fields on their habitability. With this ERC grant, I will form a team dedicated to addressing these challenging goals in a comprehensive way. Ambitious, feasible and timely, NewWorlds will allow our team to optimally exploit SPIRou to unlock our understanding of how new worlds form, and will clear the path for future follow-up programmes with major facilities like the JWST and the E-ELT.

Mots-clés : stellar magnetic fields,low mass protostars,low mass stars,exoplanets,stellar surface imaging,spectropolarimetry,high precision velocimetry,high precision photometry
Projet ERC : INSOMNIA BEAT IT (Horizon 2020, appel 2016, Proof of concept grants)
Entité participante : Royal Netherlands Academy of Arts and Sciences — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : KNAW
Type d'entité : Recherche
Financement ERC de l'entité : 150000.0 €

Major depression is among the most prevalent and burdening health hazards. Our best chance to combat the pending global pandemic is to provide preventive interventions to identified people at risk. The strongest modifiable risk factor is insomnia. Insomnia can be ameliorated by its recommended treatment, cognitive behavioural therapy (CBT), which indeed improves depression outcomes. Current health infrastructures however cannot supply CBT for the ±10% of the population suffering from insomnia. Internet-based self-help could solve this bottleneck, but current implementations utilize only a fraction of the evidence-based effective intervention possibilities, are not equipped to support personalized approaches, and do not harness the potential to support large-scale research. The current PoC project will build an Integrated Network of Sleep-Optimizing Modules for Norm-Informed Adaptive Behavioural, Environmental And Therapeutic Internet Training (INSOMNIA BEAT IT) to solve these shortcomings and allow for the implementation of personally optimized combination of self help modules to promote health in general and to prevent depression in particular. It has a high potential for widespread marketing to health professionals and companies that may want to offer the platform to their employees to promote health and well-being.

Mots-clés : ehealth,depression,prevention,insomnia,internet,individual differences