Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : UNMAKING (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : Utrecht University — Pays : Pays-Bas — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1498200.0 €

Modern capitalist societies engage destructively with the natural environment. Societal transformation to sustainability is urged, but it implies a degree of disruption of modern, capitalist ways of being and doing. Radical grassroots innovations – those that posit a profound cultural, economic, and political transformation of dominant institutions and practices - hold the potential to lead such transformation, but may be constrained by their marginal, local, small-scale character. This programme aims to understand to what extent, under what conditions, and through what processes radical grassroots innovations unmake modern, capitalist institutions and practices. This research will compare Italian and German radical grassroots innovations in agriculture to: (1) identify and categorize mechanisms of unmaking that are involved in radical grassroots innovations; (2) explain whether and how unmaking creates space for alternatives from the individual to the social-ecological level; (3) understand how mechanisms of unmaking at different levels interplay; (4) explain why unmaking may result in different outcomes in different context; (5) develop a theory of unmaking in societal transformation to sustainability.This research is ground breaking as it (1) approaches societal transformation from the perspective of unmaking of dominant institutions, rather than of the introduction of innovations, (2) mobilizes theories that have so far not been considered, and innovatively integrates theories and levels of analysis, (3) originally employs mixed methods that capture trajectories of change, and enable to generalize causal mechanisms in complex social-ecological systems. This programme will push the boundaries of our understanding of transformation to sustainability. It will generate scientific knowledge that will be relevant across the social sciences, offer a theoretical lens –unmaking-, and test a process-tracing methodological approach to stimulate interdisciplinary research.

Mots-clés : transformation to sustainability,capitalism,civil society,agriculture,grassroots innovations
Projet ERC : EMOT (Horizon 2020, appel 2017, Proof of concept grants)
Entité participante : University of Copenhagen — Pays : Danemark — Rôle : PI
Acronyme de l'entité : UCPH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 149956.0 €

Modern communication and sensing is done with radio- and microwaves on the one hand, and with optical fiber links, on the other. Radio- and microwaves are widely used in commercial and personal communication, as well as in sensing in, for example, medical diagnostics. Optical communication dominates long distance, point-to-point connections, such as internet. This action aims at the development of a miniature device for efficient conversion between these technological platforms. Within the AdG INTERFACE we have developed a novel principle for the opto-electromechnical transducer (Nature, 2014) and filed a US patent. We have further made progress in developing an integrated version of the transducer with enhanced sensitivity. Preliminary market analysis has shown high potential of our device in diverse areas, such as industrial internet of things, airborne intracommunication systems, scientific sensors, measurement instrumentation, radio astronomy, and nuclear magnetic spectroscopy. This action will include the first tests of the device in a flagship application relevant to NMR spectroscopy—a real life MRI scanner—and an extensive effort towards producing a packaged, fiber-coupled, ultralow-noise transducer ready for field demonstrations.

Mots-clés : micro opto electro mechanical system,quantum technology,transducer,sensing,nmr spectroscopy,optical communication
Projet ERC : SusDrug (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : University of Pavia — Pays : Italie — Rôle : PI
Acronyme de l'entité : UNIPV
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1400000.0 €

Modern drug discovery is facing critical challenges. Rapid advances in human biology are revealing new biomolecular targets and processes, for which existing chemical compound libraries can provide only limited success in the identification of novel bioactive agents. This deficiency has been attributed primarily to the relative lack of structural diversity within the libraries. The three-dimensional world of biological macromolecules has been continuously interrogated with generally similar planar, aromatic, and structurally simple compounds. Contemporary diversity-generating methods have never been implemented for the preparation of large libraries, as an increase in the number of diverse members requires a corresponding increase in the number of synthetic steps, or a continuous supply of different staring materials. This proposal details a strategy for developing a chemically sustainable diversification method, by tapping into our largest source of organic compounds: arenes. The proposed research aims to develop new methods that can rapidly convert simple aromatic entities into highly functionalized, complex small molecules. By integration of this strategy with many different chemical operations, numerous distinctive and independent dearomative programs will generate a diverse set of multiplex small molecules. This simplicity-to-complexity approach will provide a practical platform for the rapid, controlled access to a functionally diverse set of compounds, ranging from anticancer to anti-infective agents. This research will also deliver methods for dearomative diversification of existing aromatic compound libraries to provide new members with unique physiochemical properties. Given the broad scope of possible dearomative programs that will be developed, and the vast amount of aromatic compounds accessible, this will ultimately provide a sustainable source of diverse molecules for the next generation of compound libraries.

Mots-clés : drug discovery,drug design,chemical diversity,sustainability,medicinal chemistry,organic synthesis,dearomatization
Projet ERC : Waves (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The study of the human past
Entité participante : Max Planck Society — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : MPG
Type d'entité : Recherche
Financement ERC de l'entité : 1500000.0 €

Modern humans have colonized every possible ecological niche, with the latest expansions being those into the remote islands of the South Pacific. This success has been underpinned both by genetic adaptations to new ecological conditions and by an ever-accumulating store of technological and cultural knowledge gained through social learning. Our dual inheritance systems of genetics and culture interact in unique and unexpected ways, making human history vastly more difficult to infer than for any other species. The emerging field of gene-culture coevolution promises to provide a cohesive framework for modeling the interplay of genes and culture, and will revolutionize our understanding of human historical processes. The Waves project will build the tools necessary to establish gene-culture coevolution in the genomic-era, extending cutting-edge population genetic techniques – including spatial simulation – and allowing the natural integration of data from across genomics, archaeogenetics, archaeology and historical linguistics. We will focus on the South Pacific, a region whose past demography remains largely unresolved. While the population history of this region is relatively short it appears extremely complex, comprising not only multiple waves of colonization but also the existence of wide and ongoing interaction spheres through which both seafaring peoples and their cultures maintained long-distance connections. By generating comprehensive new ancient and present-day genome-wide datasets across Near and Remote Oceania – and analyzing them alongside historical linguistic and cultural data – the Waves project will create the first unified gene-culture coevolutionary history of the South Pacific.

Mots-clés : gene culture coevolution,demographic history
Projet ERC : ENERGYMAPS (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed matter physics
Entité participante : TU Dresden — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : TUD
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1316306.0 €

Modern optoelectronic (OE) devices such as light-emitting or photovoltaic diodes offer exciting opportunities for the future. A wide range of materials has been utilized in these devices, including among others: organic materials, inorganic quantum dots and hybrid perovskites. While the functionality, performance and device physics vary strongly from material to material and device to device, all OE devices depend on the energy levels of their individual components and the interaction of the electronic states at the various heterointerfaces. Lacking a method to map the energy levels in a device, energy level diagrams reported for most devices consist of a combination of individual energy levels for each material, which neglect interactions between the materials (that may cause interfacial dipoles and/or band bending) and do not represent the true energetic landscape. Despite this, they are routinely used for interpretation of device performance and physics. This project aims to map the energy levels in real functional devices: revealing the true nature of buried interfaces, multilayers and contacts, and to answer fundamental long-standing questions in the field of OE, such as the origin of photovoltage losses and energetics of injection/extraction contacts of devices. We will develop and utilize a “Ultra-violet photoemission spectroscopy (UPS) depth profiling” technique based on the combination of UPS with Ar gas cluster ion beam (GCIB) etching that induces minimal surface damage, on a wide range of organic, inorganic and hybrid materials and devices. We will reveal the true energy level landscapes of devices and monitor their evolution throughout the device lifetime. Furthermore, we will explore the possibility to expand the use of GCIB etching beyond UPS as a new nanofabrication technique. These studies will open new frontiers in OE research and would allow the development of novel interface engineering approaches, device architectures and material design rules.

Mots-clés : ultra violet photoemission spectroscopy,gas cluster ion beam etching,depth profiling,optoelectronic devices,heterointerfaces,energy levels
Projet ERC : ENERGYMAPS (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed matter physics
Entité participante : Heidelberg University — Pays : Allemagne — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 181625.0 €

Modern optoelectronic (OE) devices such as light-emitting or photovoltaic diodes offer exciting opportunities for the future. A wide range of materials has been utilized in these devices, including among others: organic materials, inorganic quantum dots and hybrid perovskites. While the functionality, performance and device physics vary strongly from material to material and device to device, all OE devices depend on the energy levels of their individual components and the interaction of the electronic states at the various heterointerfaces. Lacking a method to map the energy levels in a device, energy level diagrams reported for most devices consist of a combination of individual energy levels for each material, which neglect interactions between the materials (that may cause interfacial dipoles and/or band bending) and do not represent the true energetic landscape. Despite this, they are routinely used for interpretation of device performance and physics. This project aims to map the energy levels in real functional devices: revealing the true nature of buried interfaces, multilayers and contacts, and to answer fundamental long-standing questions in the field of OE, such as the origin of photovoltage losses and energetics of injection/extraction contacts of devices. We will develop and utilize a “Ultra-violet photoemission spectroscopy (UPS) depth profiling” technique based on the combination of UPS with Ar gas cluster ion beam (GCIB) etching that induces minimal surface damage, on a wide range of organic, inorganic and hybrid materials and devices. We will reveal the true energy level landscapes of devices and monitor their evolution throughout the device lifetime. Furthermore, we will explore the possibility to expand the use of GCIB etching beyond UPS as a new nanofabrication technique. These studies will open new frontiers in OE research and would allow the development of novel interface engineering approaches, device architectures and material design rules.

Mots-clés : ultra violet photoemission spectroscopy,gas cluster ion beam etching,depth profiling,optoelectronic devices,heterointerfaces,energy levels
Projet ERC : GRAPHMODE (Horizon 2020, appel 2019, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : Technical University of Munich — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : TUM
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1971296.0 €

Modern science increasingly relies on insights gained from sophisticated analyses of large data sets. An ambitious goal of such data-driven discovery is to understand complex systems via statistical analysis of multivariate data on the activity of their interacting units. Probabilistic graphical models, the topic of this project, are tailored to the task. The models facilitate refined yet tractable data exploration by using graphs to represent complex stochastic dependencies between considered variables. Models based on directed graphs, in particular, provide the state-of-the-art approach for detailed exploration of cause-effect relationships. However, modern applications of graphical models face numerous challenges such as key variables being latent (i.e., unobservable/unobserved), lacking temporal resolution in studies of feedback loops, and limited experimental interventions. Often arising in combination, these issues generally result in observed stochastic structure that cannot be characterized using the established notion of conditional independence. As a result, we are left with only a partial understanding of which aspects of a system can be inferred from the available data, and we lack effective methods for fundamental problems such as inference in the presence of feedback loops. The aim of the new project is to move beyond conditional independence structure to obtain a deeper understanding of the inherent limitations on what can be inferred from imperfect measurements, and to design novel statistical methodology to infer estimable quantities. The unique feature of the proposed work is a focus on algebraic relations among moments of probability distributions and the subtle statistical issues arising when such relations are to be exploited in practical methodology.

Mots-clés : probabilistic graphical models,multivariate statistics,causal inference,msc2010  62hxx multivariate analysis
Projet ERC : TORI (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Centre national de la recherche scientifique — Pays : France — Rôle : PI
Acronyme de l'entité : CNRS
Type d'entité : Recherche
Financement ERC de l'entité : 1785745.0 €

Modern scientific methods heavily rely on large-scale 3D simulations. However, current data production speeds are much higher than storage speeds (5 orders of magnitude on typical supercomputers). This imbalance constitutes a major bottleneck in the scientific computing pipeline, such that most of the data generated by a simulation is not saved to disk, and thus remains unvisualized, unexplored and unanalyzed. TORI addresses this data bottleneck by introducing the next generation data reduction tools for large-scale scientific 3D data. TORI’s angle of attack is based on original and important advances in Topological Data Analysis (TDA), a class of techniques popularized in scientific visualization. TORI addresses data reduction at two levels: (i) at the data level, by deriving an analysis framework for ensembles of topological objects that is inspired by optimal transport, and (ii) at the computation level, by entirely revisiting TDA to adapt it to the context of high-performance in-situ data analysis. To identify informative datasets (i), TORI will introduce efficient methods for distance computations, barycenter evaluations and trajectory analysis. To perform this analysis on-the-fly (ii), TORI will revisit TDA with task parallel algorithms, coarse-to-fine computations and TDA-aware lossy compressors. TORI will be implemented in open-source in the Topology ToolKit, a leading TDA package, and interfaced with standard scientific computing packages (VTK, ParaView). It will be integrated in simulation codes with Catalyst and evaluated on real-life use cases in climatology, geophysics and astrophysics. TORI will have a far reaching impact on all fields of science using large-scale 3D simulations. By bringing together optimal transport and TDA in an innovative coarse-to-fine model, TORI will establish TDA as a standard tool for the analysis of large-scale ensemble datasets and it will initiate a new line of research in high-performance in-situ data analysis.

Mots-clés : scientific visualization,topological data analysis,high performance computing
Projet ERC : CoMMBi (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : Università della Svizzera italiana — Pays : Suisse — Rôle : PI
Acronyme de l'entité : USI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999497.0 €

Molecular binding is a major research topic that has undoubtedly benefited of recent technological innovation, especially in the area of the so-called computational sciences. However, the predictive power of computations remains low mainly due to the poor correlation of the in-silico models with the real world. A clear example is drug discovery where the drug in vivo efficacy is seen correlated to the ligand residence time, which is hardly predictable by current computational methods. The present proposal tackles the challenge aiming at reshaping the border of the state-of-the-art simulations in molecular binding. I outline a research program that realises a vision where drug design is entrusted to ligand binding affinity and kinetics prediction, and molecular binding interactions are simulated in a realistic plasma membrane model. To achieve the ambitious goals of the research, my team will develop and apply cutting-edge computational techniques based on free-energy calculations, machine learning and multiscale molecular dynamics simulations. Evidence of the innovative nature of the developed approaches will be given by elucidating fundamental aspects of the functional mechanism of the G-protein coupled receptors (GPCRs), a pharmacologically prominent membrane protein family targeted by ~ 40% of marketed drugs. We will achieve a thorough characterization of the binding thermodynamics and kinetics of signal molecules (antagonists and agonists) that will be used by an original machine learning model to identify novel receptor antagonists with prescribed binding affinity and residence time. We will then investigate the receptor conformational transition from the inactive to the active state and develop an ad hoc multiscale approach to characterize the formation of GPCRs dimers, oligomers and clusters in cell membrane and their interaction with the G-protein that activates the signal transduction. Experiments will be performed to validate all the in-silico results.

Mots-clés : funnel metadynamics,metadynamics,molecular dynamics,ligand binding free energy,martini coarse grained molecular dynamics
Projet ERC : iGLURs - A NEW VIEW (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : University of Copenhagen — Pays : Danemark — Rôle : Partner
Acronyme de l'entité : UCPH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 0.0 €

Molecular biology strives for the prediction of function, based on the genetic code. Within neuroscience, this is reflected in the intense study of the molecular basis for ligand recognition by neurotransmitter receptors. Consequently, structural and functional studies have rendered a profoundly high-resolution view of ionotropic glutamate receptors (iGluRs), the archetypal excitatory receptor in the brain. But even this view is obsolete: we don’t know why some receptors recognize glutamate yet others recognize other ligands; and we have been unable to functionally test the underlying chemical interactions. In other words, our view differs substantially from nature’s own view of ligand recognition. I plan to lead a workgroup attacking this problem on three fronts. First, bioinformatic identification and electrophysiological characterization of a broad and representative sample of iGluRs from across the spectrum of life will unveil the diversity of ligand recognition in iGluRs. Second, phylogenetic analyses combined with functional experiments will reveal the molecular changes that nature employed in arriving at existing means of ligand recognition in iGluRs. Finally, chemical-scale mutagenesis will be employed to overcome previous technical limitations and dissect the precise chemical interactions that determine the specific recognition of certain ligands. With my experience in combining phylogenetics and functional experiments and in the use of chemical-scale mutagenesis, the objectives are within reach. Together, they form a unique approach that will expose nature’s own view of ligand recognition in iGluRs, revealing the molecular blueprint for protein function in the nervous system.

Mots-clés : glutamate receptors,ion channels,ligand recognition,evolution,ancestral reconstruction