Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : Emergence (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
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 : 1070343.0 €

Many physical or biological systems display time-dependent states which can be mathematically modelled by a differentiable dynamical system. The state of the system consists of a finite number of variables, and the short time evolution is given by a differentiable equation or the iteration of a differentiable map. The evolution of a state is called an orbit of the system. The theory of dynamical systems studies the long time evolution of the orbits. For some systems, called chaotic, it is impossible to predict the state of an orbit after a long period of time. However, in some cases, one may predict the probability of an orbit to have a certain state. A paradigm is given by the Boltzmann ergodic hypothesis in thermodynamics: over long periods of time, the time spent by a typical orbit in some region of the phase space is proportional to the “measure” of this region. The concept of Ergodicity has been mathematically formalized by Birkhoff. Then it has been successfully applied (in particular) by the schools of Kolmogorov and Anosov in the USSR, and Smale in the USA to describe the statistical behaviours of typical orbits of many differentiable dynamical systems. For some systems, called wild, infinitely many possible statistical behaviour coexist. Those are spread all over a huge space of different ergodic measures, as initially discovered by Newhouse in the 70's. Such systems are completely misunderstood. In 2016, contrarily to the general belief, it has been discovered that wild systems form a rather typical set of systems (in some categories). This project proposes the first global, ergodic study of wild dynamics, by focusing on dynamics which are too complex to be well described by means of finitely many statistics, as recently quantified by the notion of Emergence. Paradigmatic examples will be investigated and shown to be typical in many senses and among many categories. They will be used to construct a theory on wild dynamics around the concept of Emergence.

Mots-clés : ergodic theory,non uniform hyperbilicity,blender,bifurcation theory,coexistence of infinitely many attractors
Projet ERC : BDE (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Basel — Pays : Suisse — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1997510.0 €

Many problems in computer science can be cast as state-space search, where the objective is to find a path from an initial state to a goal state in a directed graph called a "state space". State-space search is challenging due to the state explosion problem a.k.a. "curse of dimensionality": interesting state spaces are often astronomically large, defying brute-force exploration. State-space search has been a core research problem in Artificial Intelligence since its early days and is alive as ever. Every year, a substantial fraction of research published at the ICAPS and SoCS conferences is concerned with state-space search, and the topic is very active at general AI conferences such as IJCAI and AAAI. Algorithms in the A* family, dating back to 1968, are still the go-to approach for state-space search. A* is a graph search algorithm whose only "intelligence" stems from a so-called "heuristic function", which estimates the distance from a state to the nearest goal state. The efficiency of A* depends on the accuracy of this estimate, and decades of research have pushed the envelope in devising increasingly accurate estimates. In this project, we question the "A* + distance estimator" paradigm and explore three new directions that go beyond the classical approach: 1. We propose a new paradigm of declarative heuristics, where heuristic information is not represented as distance estimates, but as properties of solutions amenable to introspection and general reasoning. 2. We suggest moving the burden of creativity away from the human expert by casting heuristic design as a meta-optimization problem that can be solved automatically. 3. We propose abandoning the idea of exploring sequential paths in state spaces, instead transforming state-space search into combinatorial optimization problems with no explicit sequencing aspect. We argue that the "curse of sequentiality" is as bad as the curse of dimensionality and must be addressed head-on.

Mots-clés : state space exploration,state space search,action planning,heuristic search,artificial intelligence
Projet ERC : SNICC (Horizon 2020, appel 2017, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : Board of the Swiss Federal Institutes of Technology — Pays : Suisse — Rôle : PI
Acronyme de l'entité : ETH Zurich
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2500000.0 €

Many products in the chemical, food and pharmaceutical industry are produced as powders through a crystallization process. Continuous crystallization has been for decades the process of choice for large-scale production, for instance of sugar, table salt, adipic acid, and has become more recently a key component of the new paradigm, i.e. the continuous manufacturing of active pharmaceutical ingredients. All product crystals of continuous crystallization processes are formed through secondary nucleation, which is both ubiquitous and elusive. Research has left several key questions unanswered, such as about where and how secondary nuclei are formed, either from the parent crystal due to collisions or from the solution layer around it due to fluid shear; or about the rate at which secondary nuclei are generated, with which properties, and under which conditions; or, also, about the coupling between the parent crystal microscale and the collective behavior of the ensemble of crystals suspended in a specific continuous crystallizer. The project SNICC intends to unveil the microscale mechanisms of secondary nucleation, and to bring its scientific understanding to a level where it can be exploited to model, design, operate, optimize and control continuous crystallization processes at any desired scale. It aims at creating a comprehensive knowledge about secondary nucleation at the microscale, applicable to any type of compound, and at establishing guidelines for the intensification of continuous crystallization, based on the use of newly developed full models of different types of continuous crystallizers. This will have a major impact on both the science of crystallization and the related industrial processes. Building on the recognized experimental and theoretical expertise on crystallization at all relevant scales of the PI and his lab, a team consisting of 4 PhD students and 2 postdocs will work for five years on this challenging, interdisciplinary project.

Mots-clés : continuous crystallization,secondary nucleation,population balance equations,crystal size and,shape
Projet ERC : GUPPYCon (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Life Science
Panel : Environmental Biology, Ecology and Evolution
Entité participante : University of Exeter — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1336582.63 €

Many species have independently evolved similar phenotypes in response to similar environmental challenges. This phenomenon, termed convergent evolution, reflects both the power and the limits of adaptation. However, we often do not know at what scale evolution has repeated itself: did selection act on the same genes in different populations or species, or did convergence result from selection on different genes? This is because, until recently, it has not been possible to investigate the genomic basis of evolution in most systems, limiting our understanding of the factors that facilitate or inhibit convergence and adaptation. To fully understand convergent evolution we need to query the genomic response to selection and determine genotype-phenotype links in systems where convergent adaptation is well established. The Trinidadian guppy (Poecilia reticulata) is a system that offers the opportunity to test the roles of multiple factors in convergent evolution: this species includes multiple natural and experimentally established populations that have repeatedly evolved similar phenotypes under similar predation environments. I propose to fully characterize the genomic-basis of repeated adaptive evolution in guppies. Aim 1 will identify regions that repeatedly show signatures of selection, and will contrast the nature of selection in natural and experimental populations that differ in age and levels of founding genetic diversity. Aim 2 will identify genomic regions associated with phenotypes that are known to play a significant role in local adaptation in the guppy using quantitative genetics approaches. I will then directly test the effects of candidate genes using novel functional genomic approaches, as detailed in Aim 3. Overall, this project will test whether repeated selection led to convergence at the genomic level, determine the genetic basis of convergent adaptations, and ultimately understand how convergent evolution has occurred in an important wild system.

Mots-clés : evolution,adaptation,population genomics,quantitative genetics
Projet ERC : DUALMARKETS (Horizon 2020, appel 2020, Starting 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 : 1060381.0 €

Market design uses economic theory to improve the function of real-world marketplaces, such as matching systems and auctions. In many markets, participants trade goods which are indivisible and highly heterogeneous. My focus is on settings in which participants also experience “income effects”. Income effects arise when participants face budget constraints or when transactions substantially affect participants’ wealth. Income effects are present in many important markets: from spectrum licenses and airport landing slots to jobs and houses. But designing well-functioning marketplaces for participants who experience income effects is challenging both in theory and in practice. As a result, income effects are often ignored in marketplace design, leading to unfair and inefficient outcomes. The goal of DUALMARKETS is to understand how to design fair and efficient marketplaces for participants with diverse preferences and income effects. Marketplaces work well when supply equals demand and when participants cannot improve their outcomes by renegotiating. First, I will develop powerful novel conditions on participants’ preferences which allow for income effects and guarantee that market-clearing prices always exist. Second, I will analyse to what extent outcomes in multi-sided matching markets are robust to cooperative renegotiation among unsatisfied participants. To achieve these two objectives, I will establish a Competitive Equilibrium Existence Duality and a Cooperative Solution Concept Duality between economies with and without income effects. Finally, I will explore applications of these Dualities to auction design. Specifically, I will develop several designs of sealed-bid multi-item auctions in which bidders can experience income effects. The new connections between competitive equilibrium, matching markets, and auctions developed in DUALMARKETS will substantially enrich the market design toolkit to better cope with the diversity of real-world preferences.

Mots-clés : jel  c62
Projet ERC : MIAMI (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Zurich — Pays : Suisse — Rôle : PI
Acronyme de l'entité : UZH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1375000.0 €

Market designers study how to set the "rules of a marketplace" such that the market works well. However, markets are getting increasingly complex such that designing good market mechanisms "by hand" is often infeasible, in particular when certain design desiderata (such as efficiency, strategyproofness, or fairness) are in conflict with each other. Moreover, human agents are boundedly-rational: already in small domains, they are best modeled as having incomplete preferences, because they may only know a ranking or the values of their top choices. In combinatorial domains, the number of choices grows exponentially, such that it quickly becomes impossible for an agent to report its full valuation, even if it had complete preferences. In this ERC grant proposal, we propose to combine techniques from "machine learning" with "market design" to address these challenges. First, we propose to develop a new, automated approach to design mechanisms with the help of machine learning (ML). In contrast to prior ML-based automated mechanism design work, we explicitly aim to train the ML algorithm to exploit regularities in the mechanism design space. Second, we propose to study the "design of machine learning-based mechanisms." These are mechanisms that use machine learning internally to achieve good efficiency and incentives even when agents have incomplete knowledge about their own preferences. In addition to pushing the scientific boundaries of market design research, this ERC project will also have an immediate impact on practical market design. We will apply our techniques in two different settings: (1) for the design of combinatorial spectrum auctions, a multi-billion dollar domain; and (2) for the design of school choice matching markets, which are used to match millions of students to high school every year.

Mots-clés : market design,economics and computation,mechanism design,auctions,machine learning
Projet ERC : IsoMS (Horizon 2020, appel 2015, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : Radboud University Nijmegen — Pays : Pays-Bas — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1182769.56 €

Mass spectrometry (MS) in combination with electrospray ionization (ESI) is one of the principal tools currently used to gain insight into newly developed catalytic reactions. It is used to identify key reaction intermediates and to study their structure and reactivity. This proposal is based on the combination of modern MS approaches with novel experiments in a unique cryo-trapping instrument. This combination allows the study of short-lived ionic species that cannot be studied by other known methods. Our distinguishing feature is the in situ helium-tagging of ions, which allows us to record their infrared spectra via a pre-dissociation technique. Here, we will go beyond this state-of-the-art approach in two directions: (1) The unparalleled advantage of ESI-MS is its high sensitivity to low-abundant and reactive species. The pertinent question at the heart of all reaction mechanism investigations via MS is how the ions found in the gas-phase relate to the condensed-phase reaction. We will address this question using “Delayed Reactant Labelling”, which will directly link condensed phase kinetics to the abundance of isolated gaseous ions. (2) We will take advantage of long storage times in our cryogenic linear quadrupole trap and expand the portfolio of the methods available to address mixtures of ions with the same mass. Isobaric mixtures are resolved in MS by differences in ion mobilities, i.e. the ions are separated by their mass-to-charge ratios and by their shapes. We will perform ion mobility separation directly in the trap by excitation of the ion secular motion using a resonant dipolar electric field. Further, we will combine cryo-trapping experiments with the probing or modifying of the stored ions by reactive collisions with neutral molecules. The mobility experiments and the reactivity probing will be routinely combined with spectroscopic experiments.

Mots-clés : ion chemistry,reaction intermediates,low temperature experiments
Projet ERC : B Massive (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : University of Milano-Bicocca — Pays : Italie — Rôle : PI
Acronyme de l'entité : UNIMIB
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1532750.0 €

Massive black hole binaries (MBHBs) are the most extreme, fascinating yet elusive astrophysical objects in the Universe. Establishing observationally their existence will be a milestone for contemporary astronomy, providing a fundamental missing piece in the puzzle of galaxy formation, piercing through the (hydro)dynamical physical processes shaping dense galactic nuclei from parsec scales down to the event horizon, and probing gravity in extreme conditions. We can both see and listen to MBHBs. Remarkably, besides arguably being among the brightest variable objects shining in the Cosmos, MBHBs are also the loudest gravitational wave (GW) sources in the Universe. As such, we shall take advantage of both the type of messengers – photons and gravitons – they are sending to us, which can now be probed by all-sky time-domain surveys and radio pulsar timing arrays (PTAs) respectively. B MASSIVE leverages on a unique comprehensive approach combining theoretical astrophysics, radio and gravitational-wave astronomy and time-domain surveys, with state of the art data analysis techniques to: i) observationally prove the existence of MBHBs, ii) understand and constrain their astrophysics and dynamics, iii) enable and bring closer in time the direct detection of GWs with PTA. As European PTA (EPTA) executive committee member and former I International PTA (IPTA) chair, I am a driving force in the development of pulsar timing science world-wide, and the project will build on the profound knowledge, broad vision and wide collaboration network that established me as a world leader in the field of MBHB and GW astrophysics. B MASSIVE is extremely timely; a pulsar timing data set of unprecedented quality is being assembled by EPTA/IPTA, and Time-Domain astronomy surveys are at their dawn. In the long term, B MASSIVE will be a fundamental milestone establishing European leadership in the cutting-edge field of MBHB astrophysics in the era of LSST, SKA and LISA.

Mots-clés : multimessenger astronomy,pulsar timing arrays,time domain astronomy,supermassive black holes
Projet ERC : StrongCoPhy4Energy (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed matter physics
Entité participante : École supérieure de physique et de chimie industrielles de la ville de Paris — Pays : France — Rôle : PI
Acronyme de l'entité : ESPCI PARIS-PSL
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1656250.0 €

Materials where conduction electrons experience strong correlation in their dynamics, are both a challenge to the current quantum theories of matter and a mine for possible technological applications. High-temperature copper- and iron-based superconductors and heavy-fermions with large thermoelectric responses are examples of materials with high potential impact on energy transmission and storage technologies or high-magnetic field applications. Recently, the discovery that the well known atomic "Hund's rules" have a surprisingly great and diversified influence on the conduction electrons in d-electron materials changed the traditional view of electronic correlation as a competition between kinetic energy and Coulomb repulsion, adding Hund's exchange energy as a third axis. This project, by using state-of-the-art computational techniques for correlated materials, aims at clarifying the influence of the new Hund's driven mechanisms as possible enhancers of high-Tc superconductivity and of thermoelectric and thermomagnetic properties. In particular Hund's coupling can induce the coexistence of weakly and strongly correlated conduction electrons, culminating in orbital-selective Mott insulating states or in heavy-fermionic physics. We then aim at the creation of a new class of transition-metal (d-electron) compounds reproducing the properties of the more exotic rare-earth (f-electron) heavy-fermion materials, in a tunable way. Iron-based superconductors exhibit some of these properties and will be used as a starting point for the search and exploration of new and enhanced high-temperature superconductors and thermoelectric/thermomagnetic d-electron materials. An exciting application is also proposed, motivating a possible resurgence of technological attention towards thermomagnetic materials: the coating of high-power cables in thermomagnetic materials for self-cooling, and potentially room-temperature superconduction.
Projet ERC : MIDNP (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : Weizmann Institute of Science — Pays : Israël — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1700000.0 €

Materials with specific electrical, optical or chemical properties often derive their special functions from small perturbations in their composition or structure. Thus, rational design of new functional materials demands sensitive and versatile determination of structural and compositional properties, a very difficult goal not presently available. The overarching goal of this ERC project is to develop a novel route for Magic-Angle Spinning Dynamic Nuclear Polarization (MAS-DNP) as an enabling methodology in materials science, introducing new opportunities for investigating and designing functional materials. Solid State Nuclear Magnetic Resonance (ssNMR) spectroscopy is an excellent probe for local order/disorder, but unfortunately its sensitivity is limited. DNP, a process whereby the large electron spin polarization is transferred to the nuclear spins, had greatly expanded the range of materials systems and questions that can be probed by ssNMR. However, it commonly relies on the use of exogenous nitroxide radicals, thereby limiting its utilization in materials science to nonreactive surfaces. We propose to develop Metal Ions DNP (MIDNP) utilizing paramagnetic dopants as endogenous polarization agents in the bulk. To effectively harness the electron spin polarization of the dopants for higher sensitivity, we will (a) address challenges such as the effect of bonding, spin interactions and relaxation on DNP via a mechanistic study of carefully selected dopants in energy materials; (b) Develop new techniques for NMR spectral assignment and explore alternative DNP mechanisms for paramagnetic solids; (c) Expand the approach for sensitizing the detection of surfaces and interfaces and elucidate the critical role of surface chemistry in the efficacy of energy storage materials. MIDNP will provide a novel, sensitive alternative for probing the structure and composition of new materials and will transform the utilization of ssNMR in the study of functional materials.

Mots-clés : solid state nuclear magnetic resonance spectroscopy,magic angle spinning,dynamic nuclear polarization,electrode electrolyte interface