Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : PAINSTRAT (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : University of London — Pays : Royaume-Uni — Rôle : Partner
Acronyme de l'entité : U. London
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 445829.14 €

In the last 15 years the advances in understanding the molecular mechanisms of pain have been spectacular. However, despite the >200 genes identified and the dramatic insights on transduction and transmission of nociceptive signals, very little has changed in the diagnosis and treatment of pain, at least in terms of analgesic drugs. Therefore, pain remains a major burden for the society (chronic pain alone costs the EU >€250 bns/yr). The reason is that pain is not a pattern of gene expression, but a state of neuronal activity. Therefore, the translation of mechanistic insights into effective treatments will depend critically on a better understanding of the electrophysiological changes that occur. For this reason, the European Federation of Neurological Society has called for a particular effort to identify reliable laboratory measures of pain, and having an objective biomarker for pain quantification and patient stratification would constitute a major breakthrough in medicine. To date, this need has not been satisfied. The project has two main objectives: (1) Developing neurophysiological biomarkers of pain perception: the main aim being quantification of spontaneous pain for patient management and clinical trials. (2) Developing neurophysiological biomarkers of the susceptibility to develop chronic pain: the main aim being patient stratification, development of prevention strategies, and personalised medicine for pain. We will apply novel analysis techniques to the neurophysiological information obtained using a range of techniques from a large number of healthy volunteers and patients with different types of chronic pain. Importantly, we will longitudinally study patients at high risk of developing chronic pain. We are fully confident that these goals - early diagnosis, identification of high risk patients and facilitating the development of new efficacious treatments through useful clinical trials - will positively impact the well-being of chronic pain patients.

Mots-clés : personalized medicine,patient stratification,chronic pain,neurophysiology
Projet ERC : REAL (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Institut national de recherche en sciences et technologies du numérique — Pays : France — Rôle : PI
Acronyme de l'entité : Inria
Type d'entité : Recherche
Financement ERC de l'entité : 1498830.0 €

In the last decade, machine learning (ML) has become a fundamental tool with a growing impact in many disciplines, from science to industry. However, nowadays, the scenario is changing: data are exponentially growing compared to the computational resources (post Moore's law era), and ML algorithms are becoming crucial building blocks in complex systems for decision making, engineering, science. Current machine learning is not suitable for the new scenario, both from a theoretical and a practical viewpoint: (a) the lack of cost-effectiveness of the algorithms impacts directly the economic/energetic costs of large scale ML, making it barely affordable by universities or research institutes; (b) the lack of reliability of the predictions affects critically the safety of the systems where ML is employed. To deal with the challenges posed by the new scenario, REAL will lay the foundations of a solid theoretical and algorithmic framework for reliable and cost-effective large scale machine learning on modern computational architectures. In particular, REAL will extend the classical ML framework to provide algorithms with two additional guarantees: (a) the predictions will be reliable, i.e., endowed with explicit bounds on their uncertainty guaranteed by the theory; (b) the algorithms will be cost-effective, i.e., they will be naturally adaptive to the new architectures and will provably achieve the desired reliability and accuracy level, by using minimum possible computational resources. The algorithms resulting from REAL will be released as open-source libraries for distributed and multi-GPU settings, and their effectiveness will be extensively tested on key benchmarks from computer vision, natural language processing, audio processing, and bioinformatics. The methods and the techniques developed in this project will help machine learning to take the next step and become a safe, effective, and fundamental tool in science and engineering for large scale data problems.
Projet ERC : 123STABLE (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : Center odlicnosti nizkoogljicne tehnologije zavod — Pays : Slovénie — Rôle : Partner
Acronyme de l'entité : Co not
Type d'entité : Recherche
Financement ERC de l'entité : 132000.0 €

In the last decades, significant progress has been made on understanding and controlling solid/liquid electrochemical interfaces at atomic levels. As the principles guiding the activity of electrochemical reactions are quite well established (structure-activity relationships), the fundamentals of stability are still poorly understood (structure-stability relationships). 123STABLE proposes to employ (1) identical location, (2) online monitoring and (3) modeling of noble metals based nanoparticles changes with the state-of-the-art electron microscopy equipment and online dissolution and evolution analytics using electrochemical flow cell coupled to online mass spectrometers. Projects unique methodology approach with picogram sensitivity levels, in combination with sub-atomic scale microscopy insights and simulations, promises novel atomistic insights into the corrosion and reconstruction of noble metals in electrochemical environments. This unique approach is based on observations of the same nanoparticles before and after electrochemical treatment where weak and stable atomic features and events can be recognized, followed, understood and finally utilized. Upon (1) doping, (2) decoration and/or (3) other synthetic modification of nanoparticles like a change in size and shape further stabilization is envisioned. For instance, blockage of nanoparticle vulnerable defected sites like steps or kinks by more noble metal could stop or significantly slow down their degradation. The 123STABLE project will feature platinum- and iridium-based nanostructures as a model system to introduce a unique “123” approach, as they still possess the best electrocatalytic properties for the future electrification of society through the Hydrogen economy. However, their electrochemical stability is still not sufficient. Coupled with the fact that their supply is hindered by extremely scarce, rare and uneven geological distribution, the increase in their stability is of immense importance.

Mots-clés : electrocatalysis,degradation,fuel cells,electrolyzers,electron microscopy,development of advanced electrochemical characterisation methods
Projet ERC : BAM (Horizon 2020, appel 2016, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Social World and Its Diversity
Entité participante : Vrije Universiteit Amsterdam — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : VU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2499714.0 €

In the last forty years, researchers in the Field of Migration and Ethnic Studies looked at the integration of migrants and their descendants. Concepts, methodological tools and theoretical frameworks have been developed to measure and predict integration outcomes both across different ethnic groups and in comparison with people of native descent. But are we also looking into the actual integration of the receiving group of native ‘white’ descent in city contexts where they have become a numerical minority themselves? In cities like Amsterdam, now only one in three youngsters under age fifteen is of native descent. This situation, referred to as a majority-minority context, is a new phenomenon in Western Europe and it presents itself as one of the most important societal and psychological transformations of our time. I argue that the field of migration and ethnic studies is stagnating because of the one-sided focus on migrants and their children. This is even more urgent given the increased ant-immigrant vote. These pressing scientific and societal reasons pushed me to develop the project BAM (Becoming A Minority). The project will be executed in three harbor cities, Rotterdam, Antwerp and Malmö, and three service sector cities, Amsterdam, Frankfurt and Vienna. BAM consists of 5 subprojects: (1) A meta-analysis of secondary data on people of native ‘white’ descent in the six research sites; (2) A newly developed survey for the target group; (3) An analysis of critical circumstances of encounter that trigger either positive or rather negative responses to increased ethnic diversity (4) Experimental diversity labs to test under which circumstances people will change their attitudes or their actions towards increased ethnic diversity; (5) The formulation of a new theory of integration that includes the changed position of the group of native ‘white’ descent as an important actor.

Mots-clés : majority minority context,integration,ethnic diversity
Projet ERC : ROBUST (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : Université Toulouse Capitole — Pays : France — Rôle : Partner
Acronyme de l'entité : UT Capitole
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 312500.0 €

In the last several decades, it has been extensively studied how strategic behavior of economic agents could affect the outcomes of various institutions. Game theory and mechanism design theory play key roles in understanding economic agents' possible behavior in those institutions, its welfare consequences, and how we should design economic institutions to achieve desired social objectives even if the agents behave strategically for their own interests. However, existing studies mostly focus on somewhat narrow classes of economic environments by imposing restrictive assumptions. The proposed projects aim at providing novel theoretical frameworks which enable us to study agents' behavior and desirable institutions under much less assumptions. I believe that the projects have significant relevance in policy recommendation in practice and empirical studies, even though the proposed projects are primarily theoretical. In mechanism design, most papers in the literature focus on environments with independently distributed private information. We propose two novel (robustness-based) approaches to analyze mechanism design in correlated environments, motivated by their practical and empirical relevance. The robustness brought by my approach can be useful to mitigate certain types of misspecifications in mechanism design in practice. Moreover, the desirable robust mechanisms I obtain appear to be more sensible, and hence, can be useful for empirical studies of auction and other mechanism design problems. In game theory, it is often assumed that the game to be played is common knowledge, or even with uncertainty, uncertain variables are assumed to follow a common-knowledge prior .However, in many situations in reality, those do not seem to be satisfied. Our goal is to provide a novel theoretical framework to predict players' behavior in such incompletely specified games, and to identify conditions for (monotone) comparative statics. Both could be useful in empirical studies.

Mots-clés : mechanism design,implementation,auction,robust prediction in games
Projet ERC : SPRS (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : University of Cambridge — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : U. Cambridge
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1489406.0 €

In the last several decades, two canonical theories of random surfaces have emerged. The first, so-called Liouville quantum gravity (LQG), has its roots in conformal field theory and string theory from the 1980s and 1990s. The second, so-called random planar maps (RPM), has its roots in combinatorics from the 1960s. There has been immense progress in recent years in making rigorous sense of LQG, in the study of the large-scale behavior of RPM, and developing connections between them as well as with other mathematical objects such as the Schramm-Loewner evolution (SLE). The purpose of this project is to study stochastic processes on LQG and RPM. The first part of the proposed research is focused on developing a theory of growth processes on LQG, the so-called quantum Loewner evolution (QLE). QLE, introduced in joint work with Sheffield, is a family of processes which conjecturally describe the scaling limits of discrete growth processes such as diffusion limited aggregation (DLA), the Eden model, and the dielectric breakdown model (DBM) on LQG. QLE has proved to be a powerful tool in the study of LQG and RPM and was used in joint work with Sheffield to unite LQG with gamma=sqrt(8/3) with the Brownian map, the metric space scaling limit of random planar maps. Nevertheless, the development of QLE is still in its infancy and many important problems remain to be solved. It has long been conjectured that large RPM equipped with a statistical physics model, such as percolation or a uniform spanning tree, embedded into the plane in a conformal manner should be described by a form of LQG decorated by an SLE. The embedding problem is intimately connected to understanding random walk on a RPM, which has proved to be challenging. The second part of the proposal is aimed at developing new methods to settle long-standing questions about random walk on RPM, and ultimately the embedding problem for RPM.

Mots-clés : schramm loewner evolution,gaussian free field,liouville quantum gravity,random planar maps,statistical mechanics
Projet ERC : REGFUT (Horizon 2020, appel 2020, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
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 : 2102405.75 €

In the last two decades, an information revolution in the global south has profoundly shaped the urbanisation of metropolitan regions. Global and national initiatives to adopt smart technologies in local governments, with the claim that opportunities presented by digitalisation will resolve the challenges of urbanisation – are now literally automating regional futures. This project will conduct the first comprehensive South-South investigation of the dynamics of digitalisation-as-urbanisation – the transition to automated planning processes in metropolitan regions, and its impacts on regional urbanisation. The project will conduct research in peri-urban municipalities of three rapidly growing metropolitan regions of the global south where municipal digitalisation is directed towards strategic regional planning. These municipalities face major challenges with transforming paper-based colonial and postcolonial bureaucracies into automated planning processes within highly unequal contexts, and therefore represent the wider experience of digitalisation-as-urbanisation in the global south. Through detailed ethnography, interviews and information audit trails in digitalising municipalities, the project will investigate a) the rescaling of governance to the local digitalising state; b) the territorialisation of information infrastructures; and c) territorial politics of digitalisation. It will examine how digitalisation produces new territories for regional urbanisation and how state and non-state actors are assisting, contesting and disrupting these regional futures. It will bring to fruition the applicant’s agenda setting work on postcolonial urban futures, smart cities, digital citizenships and recent work on the governance of small cities in the global south. The project will build new theories and detailed empirical evidence of southern urbanisation as both a product and a producer of the ‘information revolution’ in the global south.

Mots-clés : digitalising state,regional future,urbanisation,information revolution,municipal governance
Projet ERC : GTBB (Horizon 2020, appel 2018, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : University of Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2176702.0 €

In the modern era of complex and large data sets, there is stringent need for flexible, sound and scalable inferential methods to analyse them. Bayesian approaches have been increasingly used in statistics and machine learning and in all sorts of applications such as biostatistics, astrophysics, social science etc. Major advantages of Bayesian approaches are: their ability to model complex models in a hierarchical way, their coherency and ability to deliver not only point estimators but also measures of uncertainty from the posterior distribution which is a probability distribution on the parameter space at the core of all Bayesian inference. The increasing complexity of the data sets raise huge challenges for Bayesian approaches: theoretical and computational. The aim of this project is to develop a general theory for the analysis of Bayesian methods in complex and high (or infinite) dimensional models which will cover not only fine understanding of the posterior distributions but also an analysis of the output of the algorithms used to implement the approaches. The main objectives of the project are (briefly): 1. Asymptotic analysis of the posterior distribution of complex high dimensional models 2. Interactions between the asymptotic theory of high dimensional posterior distributions and computational complexity. We will also enrich these theoretical developments by 3 strongly related domains of applications, namely neuroscience, terrorism and crimes and ecology.

Mots-clés : bayesian nonparametrics,high dimensional models,complex models,asymptotic theory
Projet ERC : PICOGAL (Horizon 2020, appel 2020, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : Leibniz Association — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : LG
Type d'entité : Recherche
Financement ERC de l'entité : 2500000.0 €

In the modern picture of galaxy formation, baryonic feedback is critical for shaping galaxies and regulating star formation. On small scales, feedback results from transporting momentum, radiation, thermal and relativistic particles but current-day magneto-hydrodynamic simulations of galaxies and galaxy clusters often neglect or over-simplify these transport processes. Electrons transport heat and cosmic rays exchange momentum and energy with the thermal plasma but both species are erroneously assumed to diffuse along magnetic field lines. However, this is in conflict with the latest plasma simulations and observations in the solar wind and of the galactic center, which imply efficient wave-particle scatterings so that the electrons and cosmic rays are advected with whistler and Alfvén waves, respectively. We propose a coordinated multi-scale approach that combines plasma kinetic and global fluid models of particle acceleration and transport in galaxies and galaxy clusters with unprecedented accuracy. In particular, we will run novel plasma simulations of shocks at supernovae and galaxy clusters, and study the plasma-wave mediated transport of electrons and cosmic rays. We will employ information field theory to coarse grain these models to derive effective transport coefficients, which will be implemented in macroscopic fluid models of cosmic ray transport and thermal conduction. Simulating feedback by cosmic rays, radiation and supernovae in cosmologically forming galaxies on scales from dwarfs to our Milky Way provides transformative changes of the physics accuracy of these models. This is complemented by cosmological galaxy cluster simulations with improved physics to understand the origin of the cluster-core bimodality, giant radio relics and halos. Comparing mock multi-frequency observables from radio to gamma-rays to data enables falsification or validation of the underlying plasma models and represents a major step towards predictive galaxy formation.

Mots-clés : plasma astrophysics,computational astrophysics
Projet ERC : PhysSF (Horizon 2020, appel 2015, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : Max Planck Society — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : MPG
Type d'entité : Recherche
Financement ERC de l'entité : 2495000.0 €

In the past decade we learned when and where stellar mass was built up in galaxies through cosmic time, now we must understand the physical causes in order to answer `How do galaxies form and evolve?’. This ERC project is designed to greatly advance our understanding of the physics of the star formation (SF) process and its regulation in typical star forming galaxies. The ERC project consists of 2 complementary parts: (A) an unparalleled characterization of the SF process in nearby galaxies through full exploitation of the revolutionary capabilities of the latest millimeter interferometers (ALMA) and optical integral field units (MUSE). This study will constrain the key physical parameters for the SF process on only 50pc scales - the scale of large HII regions and their predecessors, giant molecular clouds. At this crucial scale, the MUSE-ALMA-HST Survey will provide a characterization of the SF history, stellar/gaseous surface densities, metallicities of stars and gas, the stellar radiation field, extinction, and stellar/gas kinematics, and thus uncover the physical conditions that control and regulate the SF process. Part (B) will place the results of part (A) in a cosmological context, by characterizing key galaxy quantities (e.g., gas mass fraction, specific SF rates, gas depletion times) in fully representative galaxy samples after (z<3) and before (z>3) the peak epoch of cosmic star formation density. In addition to providing the critically needed constraints on the conditions that govern the SF process, this ERC project will provide the observational benchmark for state-of-the art galaxy simulations and models. The PI is internationally recognized as a leader in SF studies in nearby and distant galaxies, and has successfully led large international collaborations that strongly shaped our current understanding of the SF process. Through her track record and access to the required data, the PI is uniquely positioned to successful lead this ambitious program.

Mots-clés : star formation processes,feedback,multiple phases of the ism