Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : LearningStructurE (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and Analytical Chemical Sciences
Entité participante : Université de Stockholm — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1867187.0 €

Nearly half a million known chemicals have been deemed relevant for exposure studies and an even larger number of their transformation products are likely to co-occur in the environment. This mind-blowing number of possible chemical structures makes it impossible to in-silico generate all these structures, let alone synthesise and analytically confirm them, thereby limiting the discovery of novel chemicals. Today, the structural elucidation of chemicals detected with high resolution mass spectrometry relies on databases and machine learning models trained on the known chemical space. Both are fundamentally ill-suited for discovering novel chemical structures. As a result, only a few percent of the toxic activity of the environmental samples is explained by the currently known and monitored chemicals. It is crucial to access the novel chemical space to improve our understanding of the origin, fate, and impact of these chemicals. The aim of LearningStructurE is to turn the discovery of novel chemical structures from serendipity to routine. As a steppingstone in this pursuit, I will combine the fundamental understanding of chromatography and high resolution mass spectrometry with machine learning to pinpoint novel toxic chemical structures based on their empirical analytical information. To significantly advance the predictive power of machine learning models for empirical analytical information, I will take advantage of the candidate structures as a sample specific training set for machine learning models. The improved predictive power will feed into in-silico structure generation, allowing to elucidate the structure directly from the empirical analytical information. LearningStructurE will pave the way for exploration of the unknown chemical space detected from environmental samples, and thereby improve our understanding of the emissions, chemical processes transforming the emitted chemicals, and close the gap in measured and explained toxicity.

Mots-clés : environmental analysis,nontarget screening,generative models,semi supervised learning,active learning
Projet ERC : HEAL (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Soci al World and Its Interactions
Entité participante : Université de Stockholm — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1997500.0 €

The recent increase in intergroup conflicts emphasize the importance of finding ways how to reconcile members of adversary groups. Subsequently, social psychologists have developed evidence-based reconciliation interventions aimed at improving intergroup attitudes and behaviours towards outgroups. Even though intervention effectiveness and outcomes depend on the context in which people live, most empirical research on reconciliation interventions has ignored the impact of the social context. The overarching aim of this proposal is to systematically examine the influence of theoretically crucial and pre-existing contextual features on effectiveness, processes and durability of reconciliation interventions: a. degree of conflict and perceived threat, b. degree of integration vs. segregation within communities in which people live, c. ethnic majorities vs. minorities, and d. social norms on outgroup treatment. The project consists of four sequential work-packages. In WP1 and WP2, I will conduct large-scale longitudinal (online) studies in four European countries affected by conflict. These studies will test and compare the effectiveness, processes and durability of established (WP1) and context-adapted (WP2) reconciliation interventions on intergroup attitudes and real-life behaviours of ethnic majorities and minorities living in different social contexts. In WP3 and using field experiments, most effective and context-adapted reconciliation interventions will be incorporated into participants’ real life through intergroup contact and multi-media. After establishing the impact of the social context in realistic settings and using heterogenous representative samples, in WP4, I will develop and empirically evaluate the first theoretical framework for intergroup reconciliation. This project will identify effective reconciliation interventions that fit a variety of social contexts and thus provide concrete and effective tools for peace-building agents.

Mots-clés : intergroup reconciliation social psychological interventions contextual contact prejudice reduction
Projet ERC : Mobility (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : Université de Stockholm — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1776805.0 €

A society where individuals can pursue their dreams, striving to become whatever they desire, excels both in efficiency and equity. However, throughout history, following parents' occupations was the norm, changing social class an exception. The question remains: what caused the increase in social mobility? My research program aims to answer these questions with three interconnected projects. Project I tackles the main obstacle to studying mobility: the availability of long-run individual-level panel data of parents and children. Digitizing an overlooked series of historical books, I will build a unique dataset of incomes and occupations of the universe of Swedish taxpayers since 1905, matched with panel data of firms with their revenue, payroll, and top executives since 1857. The novelty lies in the universal coverage of data, spanning the birth of prosperous, innovative, and inclusive Sweden with an advanced welfare state.  Project II leverages this comprehensive dataset to establish stylized facts about mobility in labor and capital incomes, as well as in occupation and entrepreneurship. They will shed light on the long-run patterns of mobility and its associations with inequality and growth, resolve long-standing controversies, and bring new insights into the debate. Project III develops a novel theoretical model of mobility that uncovers the drivers of mobility. The model shows that social mobility is driven by the tension between opportunities for social climbers to access physical and human capital and the privileges of the elite to hinder such access. We exploit quasi-experimental evidence to test this theory, using the expansion of private banks and public education, as well as the rise and fall of progressive taxation. Together, the projects build micro-founded connections between mobility, inequality, and growth. The drivers of mobility we uncover may bolster both mobility and growth, challenging the traditional efficiency and equity trade-off.

Mots-clés : public economics,inter generational mobility,inequality,social mobility
Projet ERC : HIPSTERMacro (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : Université de Stockholm — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1504353.0 €

This project will develop macroeconomic models with household and firm heterogeneity to investigate the importance of housing, inequality, and price-setting in macroeconomic fluctuations and the transmission and efficacy of policy. The objective is to provide a modeling framework that is consistent with both empirical microeconomic evidence on household and firm behavior and macro evidence on the response of the aggregate economy to economic shocks. A central theme will be on non-linearities and large shocks. Computational methods for solving heterogeneous agent models non-linearly in the sequence space will be developed. The ultimate goal is to help make these models the new standard for the study of fluctuations and policy evaluation in macro. Inequality and incomplete markets will be a central theme. The first objective is to quantify how changes in monetary policy redistribute resources across households and demographic groups, including gender, race, and ethnicity. The project will also focus on the price-setting behavior of firms by studying state-dependent pricing models. It will show how shifts in nominal demand can lead to shifts in the New Keynesian Phillips Curve. Next, the project will study monetary and fiscal policy in a high-inflation environment when incomplete markets and state-dependent pricing interact. The project will also focus on housing and mortgage debt as an amplification and propagation mechanism. The project will develop a rich benchmark model of housing for policy evaluation. First, the project will investigate how housing and debt heterogeneity affect monetary policy transmission. Next, the state-dependent pricing model will be integrated into the housing model to perform a cross-country analysis to quantify the importance of different arrangements in the mortgage market for the economy's response to the inflationary episode post-COVID pandemic.

Mots-clés : heterogeneity,incomplete markets,housing,inequality,monetary policy,fiscal policy,phillips curve
Projet ERC : INcEPTION (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and Communication Engineering
Entité participante : Université de Brescia — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999533.75 €

Neurorehabilitation technologies aim to promote functional neural plasticity in patients suffering from neuromuscular disabilities. Non-invasive systems for the magnetic or electrical stimulation of the neural pathways are particularly promising for interfacing directly with the nervous system of the patients and restoring coordinated movements. Despise these potentials, current neurorehabilitation systems have still significant limitations since they usually employ stimulation protocols based on specific assumptions on cortical and spinal connectivity. An efficient way to overcome this problem will be to estimate neural adaptations during the rehabilitation procedure and optimize its parameters directly. The project INcEPTION aims to develop innovative methods to estimate patterns of neural connectivity from the decomposition of high-density surface EMG signals and induce reorganization of the connectivity of motoneuron populations innervating the main arm and shoulder muscles using magnetic and electrical stimulation of cortical and sensory pathways. The revolutionary concept of the project will be to “implant” a signature of motoneuron correlation to promote changes in neural connectivity and, therefore, functional recovery of movements in chronic stroke individuals and breast cancer survivors. The approach will provide the possibility to better understand the mechanisms of neuroplasticity in the central nervous system and define efficient stimulation protocols to re-establish natural connectivity in the motoneuron pools of patient individuals. By combining multi-disciplinary contributions from the fields of neurorehabilitation, computational neuroscience, biomedical signal processing, and neurophysiology, the project INcEPTION aims to produce substantial progress toward a better understanding of the adaptation mechanisms involved in the connectivity of spinal motor neurons and its use in the next generation of neurorehabilitation systems.

Mots-clés : high density surface emg,motor unit decomposition,neurorehabilitation,rtms,sensory stimulation,correlation,pathological synergies
Projet ERC : SkEyes (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and Communication Engineering
Entité participante : Université de Chypre — Pays : Chypre — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2346219.0 €

Over the past two decades, we witnessed impressive advancements in Robotics. Amongst the most disruptive developments was the demonstration of small Unmanned Aerial Vehicles (UAVs) equipped with onboard cameras conducting autonomous, vision-based flights without reliance on GPS, sparking booming interest in a plethora of use-cases, such as automation of drone delivery, and infrastructure inspection and maintenance. This led to the emergence of new algorithms, advanced sensors, as well as miniaturized, powerful chips, opening exciting opportunities for automating single- as well as multi-UAV navigation. Current solutions, however, lack greatly in robustness and generality, struggling to perform outside very controlled settings, with onboard perception constituting the biggest impediment. Working in this area for over a decade, it is troubling that despite dramatic progress, we still lack the technology to enable a UAV swarm to autonomously scan the seas for refugee dinghies or forest areas for wildfires, and to provide help in such dire situations. While, in principle, the core technology is the same across all use-cases, battling adverse conditions, such as wind, smoke, and degraded illumination, render the latter use-cases extremely challenging as they are time-critical and cannot be postponed until favorable conditions arise. Employing some of the currently most promising sensors, such as lidar, and advanced depth, thermal, event, and visual cameras, SkEyes proposes to address fundamental research questions to understand how to process and combine these heterogeneous sensing cues onboard a swarm of small UAVs. The goal is to achieve joint spatial understanding and scene awareness for effective autonomy in highly dynamic and realistic scenarios. Engaging such eyes in the sky, the focus is on robust, collaborative perception to enable intelligent UAV swarm navigation exhibiting adaptability in completing a mission in challenging conditions, at the push of a button.

Mots-clés : robotic perception,uav navigation,computer vision,semantic scene understanding,path planning
Projet ERC : EMetBrown (Horizon Europe, appel 2021, 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 : 1999348.75 €

Soft and wet contacts are ubiquitous across scales from geology to physiology and are crucial for engineering. Furthermore, many processes of physics and biology at small scales are governed by the mobility of microscopic entities in soft and confined environments, with the aim of reaching specific targets. Interestingly, an emergent elastohydrodynamic (EHD) lift force was theoretically predicted recently for an immersed object moving near an elastic wall. An active community, including the PI, has started to explore this striking effect with various deterministic models and experiments, showing its relevance for nanoscale and biomimetic systems. In this context, and moving beyond the deterministic, the PI’s central claim is that such EHD effects can be spontaneously triggered by thermal fluctuations. The result would be an original migration scenario in complex and confined environments – with enormous implications. However, studies are scarce on the topic. The ambition of EMetBrown is thus to address this challenge at the interface between two mature fields, by solving a fundamental problem involving both continuum and statistical mechanics: Brownian motion near soft interfaces. The three objectives are to reveal, explore and harvest the signatures of such motion, paving the way towards the future design of methods for particle transport, surface patterning, confined reactions and nanorheology. These objectives will be reached using a combination of experiments, theory and numerics, domains in which the PI has extensive experience. EMetBrown involves three core experimental setups (free colloids, optical trapping and atomic-force microscopy), three core theoretical models (soft lubrication, stochastic theory and Langevin simulations) and three exploratory tools (microfluidics, suspension rheometry and molecular dynamics). These complementary methods will be employed through four work packages covering various viscous, hard, soft materials, as well as applied flow.

Mots-clés : soft matter,stokes flows,interfacial flows,thin films,lubrication,soft lubrication,contact mechanics,surface forces,elastic materials,colloids,brownian motion,diffusion,fluctuations,biological motility,rheology,elastomers,gels,microfluidics,nanofluidics
Projet ERC : DITTO (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental Constituents of Matter
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 : 1999409.0 €

While the Standard Model of particle physics (SM) has proven tremendously successful, experimental evidence points to it not being a complete description of our universe, but a low energy approximation of a more complete theory. The searches for new phenomena (NP) are therefore an important component of the experimental program of the Large Hadron Collider (LHC). While there is no direct evidence for NP at the LHC, measurements of beauty hadron decays display a seemingly coherent pattern of deviations with respect to the SM predictions, which suggest that NP couples differently to three generations of matter. The quark-level processes responsible for these so-called ‘flavor anomalies’ are related to dilepton production processes (Drell-Yan scattering) through the crossing symmetry. The measurements of the kinematics of the high mass Drell-Yan (HMDY) process are therefore uniquely sensitive to a wide variety of NP explanations of the ‘anomalies’.  The goal of this ground-breaking project is to provide for the first time the complete set of world’s most precise HMDY differential cross-section measurements covering not only the light lepton channels ev/μv/ee/µµ, but also extremely challenging third generation τv and ττ, ditau (DITTO), final states. These measurements will be used to probe NP at mass scales beyond the reach of direct production at the LHC through the SM effective field theory framework. The innovative performance and trigger improvements of the DITTO project will allow the ensemble of the proposed measurements to reach maximum precision, boosting the possibility for a NP discovery within the data sets expected to be collected by the ATLAS experiment at the LHC. Just as dilepton resonances, such as the J/ψ and Y mesons and the Z and W bosons were crucial for the establishment of the SM, the study of the same final state, to be undertaken in the DITTO project, will help to pave the way for a better understanding of the physics processes beyond it.
Projet ERC : SunFlower (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and Analytical Chemical Sciences
Entité participante : Université de Szeged — Pays : Hongrie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999750.0 €

To be the first CO2-neutral continent by 2050, Europe needs to develop and implement disruptive new technologies, based on scientific breakthroughs. In this regard, utilization of CO2 and organic waste as feedstock to generate valuable products will play a key role in turning the chemical industry on a more sustainable, circular path. In the SunFlower project, we are going to demonstrate that two high-value processes (CO2 or CO reduction and glycerol oxidation will be studied first) can be synergistically coupled to produce chemicals (such as ethylene and lactic acid) and fuels, using novel photoelectrode assemblies (both photocathodes and photoanodes), original photoelectrochemical (PEC) device architectures, and automated processes. The SunFlower project is based on the following three hypotheses: 1. Proper engineering of continuous-flow PEC cells operating under concentrated sunlight will allow current densities similar to the electrochemical (EC) methods. 2. One semiconductor alone can supply the necessary energy input for bias-free operation of PEC cells, while generating two high-value products. 3. PEC methods can provide superior selectivity compared to their EC counterparts, even at high current density operation (as the current density and potential can be decoupled). To validate our hypotheses, we are going to use for the first time: • The pairing of two high-value generating redox processes (none of them being H2 or O2 evolution). • Concentrated sunlight (which has only been used for water-splitting so far). • Custom-designed and developed PEC cells, elaborating on the photo-gas diffusion electrode concept. • Machine learning, based on the broad dataset collected by the sensors built in the PEC system, optimizing the performance at a system level. The proposed combination of these novel approaches will be of groundbreaking nature, therefore, it opens a whole new arena of solar energy conversion.

Mots-clés : solar energy conversion,photoelectrochemistry,circular economy
Projet ERC : ArOMa (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Texts and Concepts
Entité participante : Université de Pise — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1555956.0 €

The present project is focused upon a group of seminal sources, part and parcel of the formation of intellectual discourse in the Middle Ages, and forming a common basis of understanding between the cultures on both sides of the Mediterranean, until the today. One of the prominent features of Medieval Aristotelianism, both Arabic and Latin, is the fact that Aristotle has been credited with writings that, albeit different in origin and sometimes of differing philosophical affiliations, circulated under his name. The Project Aristotle One and Many. The Arabic Pseudepigrapha (ArOMa) is focused on the works falsely attributed to Aristotle in the Arabic philosophical literature translated or forged in the 9th-10 cent. The corpus of ‘Pseudo-Aristotelica Arabica’ includes two categories of texts: A. Arabic versions of works falsely attributed to Aristotle already in Greek. B. Medieval Arabic forgeries. To these will be added: C. Commentaries on such works (including abridgements, paraphrases etc.). The aim is to provide a new insight into the Arabic Aristotle through the first overall treatment of the Arabic Pseudo-Aristotelica, including an inventory of the manuscript sources, editions and translations of the most relevant items, tools and studies towards a better understanding of Aristotle’s reception in the Arabic-speaking world. Also the Latin versions of such pseudepigrapha will be taken into account. It is still uncertain how many works falsely attributed to Aristotle are mentioned in the Arabic sources, how many have survived or have been lost. Few have been edited or translated. ArOMa fills this gap, adding a new dimension to the standard historiographical accounts. We expect to provide a more complex, and unorthodox image of Aristotle’s facets presenting an engaging and thorough history of the Aristotelian philosophy during the centuries which correspond to the Latin Middle Ages.

Mots-clés : arabic aristotle,history of medieval philosophy,arabic pseudo aristotelica,aristotelianism in the arabic tradition,late ancient commentators