Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : GRADIENTSENSING (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Life Science
Panel : Cellular, Developmental and Regenerative Biology
Entité participante : Institute of Science and Technology Austria — Pays : Autriche — Rôle : PI
Acronyme de l'entité : ISTA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1984922.0 €

Gradients of extracellular signalling molecules are a central concept in biology: for example gradients of guidance-cues such as chemokines position migrating cells in development, malignancy and immunity. Because immune cells are permanently motile, their function most critically depends on spatiotemporal orchestration by a large family of chemokines. To specify direction, concentration differences of the chemokine need to be interpreted by the migrating cell. Most mechanistic knowledge about eukaryotic gradient sensing is inferred from the amoeba Dictyostelium discoideum migrating towards soluble gradients of cyclicAMP. The biology of chemokines is much more diverse, e.g. gradients can take different shapes and, importantly, they do not only emerge in the soluble but also in the immobilized phase. In this proposal we suggest to address the principles of leukocyte chemotaxis using convergent system wide, cell biological and intravital approaches. Employing a newly developed, genetically tractable primary leukocyte system, we will test the contribution of spatial and temporal signalling paradigms of gradient sensing. Quantitative microscopy will be used to image cellular responses to engineered immobilized and soluble chemokine gradients of defined shape as well as to optogenetically triggered signals. In a complementary approach we will screen for proteins responding to chemokine signalling and perform the first genome wide genome editing-based loss of function screen for directionally persistent chemotaxis and haptotaxis. Findings will be validated in vivo to guarantee physiological relevance. In a support project we will precision-engineer the genome of primary leukocytes suitable for assaying migration. A unique combination of cellular, genetic, engineering and quantitative microscopy tools will allow this new and holistic approach to a question which is not only fundamental for immunology but also for understanding development and cancer biology.

Mots-clés : chemotaxis,migration,leukocyte,chemokine,dendritic cell,gradient sensing,g protein coupled receptor signalling
Projet ERC : SSiGraph (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : University of Warwick — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1226107.29 €

Graph Theory is a highly active area of Combinatorics with strong links to fields such as Optimisation and Theoretical Computer Science. A fundamental meta-problem in Graph Theory is the following: given a graph H, what conditions guarantee that another graph G contains a copy of H as a subgraph? This is particularly important when H is spanning, i.e. where G and H have the same number of vertices. This project will address a range of exciting and challenging extremal and probabilistic problems on spanning subgraphs in graphs, in the following two interrelated areas: 1. Spanning subgraphs in random graphs: A key aim of Probabilistic Combinatorics is to determine the density threshold for the appearance of different subgraphs in random graphs. This is particularly difficult when the subgraph is spanning, where the known results and techniques are typically highly specific. This project will lead to a unified paradigm for studying thresholds of spanning subgraphs by introducing and developing a new coupling technique. This will provide an excellent platform to study the Kahn-Kalai conjecture, a bold general conjecture on appearance thresholds, and problems including hitting-time conjectures and universality problems. 2. Spanning subgraphs in coloured graphs: Many different combinatorial problems are expressible using edge coloured graphs, including Latin square problems dating back to Euler. My objectives here concern long-standing problems on spanning trees, cycles and matchings, and, through this, the resolution of several famous labelling and packing problems. In preliminary work I have developed techniques to study these problems, techniques which will have a far reaching impact, and certainly lead to further applications, e.g. with hypergraphs and resilience problems. The objectives represent a carefully selected range of related major outstanding problems, whose solution would mark truly significant progress in the field.

Mots-clés : extremal graph theory  probabilistic graph theory  spanning subgraphs  thresholds in random graphs  edge coloured graphs  embedding trees,cycles and matchings  graph decomposition  randomised embeddings  deterministic embeddings  probability couplings  regularity techniques
Projet ERC : SYSTEMATICGRAPH (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Max Planck Society — Pays : Allemagne — Rôle : Partner
Acronyme de l'entité : MPG
Type d'entité : Recherche
Financement ERC de l'entité : 170000.0 €

Graph-theoretical models are natural tools for the description of road networks, circuits, communication networks, and abstract relations between objects, hence algorithmic graph problems appear in a wide range of computer science applications. As most of these problems are computationally hard in their full generality, research in graph algorithms, approximability, and parameterized complexity usually aims at identifying restricted variants and special cases, which are at the same time sufficiently general to be of practical relevance and sufficiently restricted to admit efficient algorithmic solutions. The goal of the project is to put the search for tractable algorithmic graph problems into a systematic and methodological framework: instead of focusing on specific sporadic problems, we intend to obtain a unified algorithmic understanding by mapping the entire complexity landscape of a particular problem domain. Completely classifying the complexity of each and every algorithmic problem appearing in a given formal framework would necessarily reveal every possible algorithmic insight relevant to the formal setting, with the potential of discovering novel algorithmic techniques of practical interest. This approach has been enormously successful in the complexity classifications of Constraint Satisfaction Problems (CSPs), but comparatively very little work has been done in the context of graphs. The systematic investigation of hard algorithmic graph problems deserves the same level of attention as the dichotomy program of CSPs, and graph problems have similarly rich complexity landscapes and unification results waiting to be discovered. The project will demonstrate that such a complete classification is feasible for a wide range of graph problems coming from areas such as finding patterns, routing, and survivable network design, and novel algorithmic results and new levels of algorithmic understanding can be achieved even for classic and well-studied problems.

Mots-clés : f 2 2  theory of computing   analysis of algorithms and problem complexity   computation on discrete structures,g 2 2  mathematics of computing   discrete mathematics    graph theory
Projet ERC : e-Sequence (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : University of the Basque Country — Pays : Espagne — Rôle : PI
Acronyme de l'entité : UPV;EHU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 311472.49 €

Graphene nanoribbons (NR) are quasi-1D nanostructures with discrete band gaps, ballistic conduction, and one-atom thickness. Such properties make them ideal candidates to develop low-dimensional semiconductors, which are essential components in nanoelectronics. Atomically-precise control over the structure of NR (width, length, edge, doping) is crucial to fully exploit their potential. However, current approaches for the synthesis of NR suffer from several drawbacks that do not allow attaining such level of precision, therefore alternative methods need to be sought. e-Sequence will develop an unprecedented approach that assembles stepwise small molecular building blocks into NR to specifically target the most important challenges in NR synthesis. Such approach will enable the preparation of an unlimited number of NR with atomically-precise control over their structure and with almost no synthetic and purification effort, exceeding the limits of existing methods. The impact of e-Sequence will not be limited to NR synthesis but it will also extend to other disciplines, since NR are promising candidates to develop new technologies with applications in electronics, sensing, photonics, energy storage and conversion, spintronics, etc. e-Sequence ambitious research programme will be orchestrated by an independent scientist with an excellent track record of achievements in low-dimensional carbon nanostructures, and who has already established a fledgling and internationally competitive research group. Building on this and on his recent permanent appointment as Research Professor, the award of this ERC project will enable him to consolidate his group, build a portfolio of excellent research, and produce results that compete on the world stage.

Mots-clés : carbon nanostructures,carbon nanoforms,nanocarbons,fullerenes,carbon nanotubes,heteroatom doped nanocarbons
Projet ERC : e-Sequence (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Basque center for macromolecular design and engineering polymat fundazioa — Pays : Espagne — Rôle : Partner
Acronyme de l'entité : Polymat
Type d'entité : Recherche
Financement ERC de l'entité : 1688527.51 €

Graphene nanoribbons (NR) are quasi-1D nanostructures with discrete band gaps, ballistic conduction, and one-atom thickness. Such properties make them ideal candidates to develop low-dimensional semiconductors, which are essential components in nanoelectronics. Atomically-precise control over the structure of NR (width, length, edge, doping) is crucial to fully exploit their potential. However, current approaches for the synthesis of NR suffer from several drawbacks that do not allow attaining such level of precision, therefore alternative methods need to be sought. e-Sequence will develop an unprecedented approach that assembles stepwise small molecular building blocks into NR to specifically target the most important challenges in NR synthesis. Such approach will enable the preparation of an unlimited number of NR with atomically-precise control over their structure and with almost no synthetic and purification effort, exceeding the limits of existing methods. The impact of e-Sequence will not be limited to NR synthesis but it will also extend to other disciplines, since NR are promising candidates to develop new technologies with applications in electronics, sensing, photonics, energy storage and conversion, spintronics, etc. e-Sequence ambitious research programme will be orchestrated by an independent scientist with an excellent track record of achievements in low-dimensional carbon nanostructures, and who has already established a fledgling and internationally competitive research group. Building on this and on his recent permanent appointment as Research Professor, the award of this ERC project will enable him to consolidate his group, build a portfolio of excellent research, and produce results that compete on the world stage.

Mots-clés : carbon nanostructures,carbon nanoforms,nanocarbons,fullerenes,carbon nanotubes,heteroatom doped nanocarbons
Projet ERC : GWmining (Horizon 2020, appel 2020, Starting 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 : 1499917.0 €

Gravitational-wave astronomy is entering its large-statistics regime. Catalogs with thousands of gravitational-wave events will soon be available, providing a wealth of information on the most compact objects in the Universe --black holes and neutron stars. These new datasets need new tools to be exploited effectively in order to maximize their scientific impact. GWmining is an ambitious program to explore upcoming gravitational-wave catalogs with data-mining techniques. We will develop a complete framework to analyze gravitational-wave data in light of astrophysical predictions. Going beyond phenomenological models, we will train machine-learning algorithms directly on large banks of population-synthesis simulations and post-Newtonian integrations. The development of these astrophysical predictions requires new modeling strategies to accurately capture all the gravitational-wave observables, notably spins and eccentricities. Combined with a hierarchical Bayesian analysis, our neural network will deliver the most stringent measurements to date on elusive phenomena influencing the lives of massive stars. We will constrain phenomena such as binary common envelope, supernova kicks, stellar winds, tidal interactions, etc. Besides harnessing the catalog in its entirety, our complete framework will put us at the forefront to analyze outliers --golden events with favorable properties of one or more parameters. We will design a complete strategy to exploit the strongest signals to infer exquisite details of the relativistic dynamics of their sources. GWmining is a unique project strategically placed at the intersection of astronomy, data analysis, and relativity. As the large-statistics revolution of gravitational-wave astronomy unfolds, GWmining will pioneer the application of data-mining techniques in gravitational-wave population studies, setting the foundations of this booming field for decades.

Mots-clés : gravitational waves,black holes
Projet ERC : MaGRaTh (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : University of Cambridge — Pays : Royaume-Uni — Rôle : Partner
Acronyme de l'entité : U. Cambridge
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 283192.0 €

Gravity is the weakest but the most intriguing fundamental interaction in the Universe. In the last decades a formidable intellectual effort has shown that the full-fledged geometric nature of gravity offers much more than a beautiful description and understanding of all stellar and galactic. In the quest for the ultimate theory of gravity, new and spectacular connections between high-energy physics, astrophysics, cosmology and theoretical physics have emerged. Triggered by breakthroughs at the observational, experimental and conceptual levels, strong gravity physics is experiencing a Golden Age, making it one of the most active fields of research of the 21st century. My group in Lisbon has been involved in groundbreaking research into the nature of strong-field effects in curved spacetime with applications in various fields, thus establishing international leadership in the field. This proposal aims at understanding, via perturbative techniques and full-blown nonlinear evolutions, the strong-field regime of gravity, and includes challenging nonlinear evolutions describing gravitational collapse, compact binary inspirals and collisions in the presence of fundamental fields. The proposed programme will significantly advance our knowledge of Einstein's field equations and their role in fundamental questions (e.g. cosmic censorship, hoop conjecture, spacetime stability, no hair theorems), but also its interplay with high energy, astro and particle physics (testing the precise nature of the interaction between compact objects and matter --such as dark matter candidates or accretion disks-- and its imprint on gravitational wave emission, understanding gravitational-led turbulence,etc). This is a cross-cutting and multidisciplinary program with an impact on our understanding of gravity at all scales, on our perception of black hole-powered phenomena and on gravitational-wave and particle physics.

Mots-clés : einstein field equations,gauge gravity duality,black hole physics,gravitational waves
Projet ERC : DISEASE (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Life Science
Panel : Environmental Biology, Ecology and Evolution
Entité participante : University of Fribourg — Pays : Suisse — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 22712.5 €

Group-living has been predicted to have opposing effects on disease risk and immune strategies. First, since repeated contacts between individuals facilitate pathogen transmission, sociality may favour high investment in personal immunity. Alternatively, because social animals can limit disease spread through collective sanitary actions (e.g., mutual grooming) or organisational features (e.g., division of the group’s social network into distinct subsets), sociality may instead favour low investment in personal immunity. The overall goal of this project is to experimentally test these conflicting predictions in ants using advanced data collection and analytical tools. I will first quantify the effect of social organisation on disease transmission using a combination of automated behavioural tracking, social network analysis, and empirical tracking of transmission markers (fluorescent beads). Experimental network manipulations and controlled disease seeding by a robotic ant will allow key predictions from network epidemiology to be tested, with broad implications for disease management strategies. I will then study the effect of colony size on social network structure and disease transmission, and how this in turn affects investment in personal immunity. This will shed light on far-reaching hypotheses about the effect of group size on social organisation ('size-complexity’ hypothesis) and immune investment (‘density-dependent prophylaxis’). Finally, I will explore whether prolonged pathogen pressure induces colonies to reinforce the transmission-inhibiting aspects of their social organisation (e.g., colony fragmentation) or to invest more in personal immunity. This project will represent the first empirical investigation of the role of social organisation in disease risk management, and allow its importance to be compared with other immune strategies. This will constitute a significant advance in our understanding of the complex feedback between sociality and health.

Mots-clés : social organisation,social network analysis,epidemiology,disease transmission,immune investment
Projet ERC : DISEASE (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Life Science
Panel : Environmental Biology, Ecology and Evolution
Entité participante : University of Bristol — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1477282.5 €

Group-living has been predicted to have opposing effects on disease risk and immune strategies. First, since repeated contacts between individuals facilitate pathogen transmission, sociality may favour high investment in personal immunity. Alternatively, because social animals can limit disease spread through collective sanitary actions (e.g., mutual grooming) or organisational features (e.g., division of the group’s social network into distinct subsets), sociality may instead favour low investment in personal immunity. The overall goal of this project is to experimentally test these conflicting predictions in ants using advanced data collection and analytical tools. I will first quantify the effect of social organisation on disease transmission using a combination of automated behavioural tracking, social network analysis, and empirical tracking of transmission markers (fluorescent beads). Experimental network manipulations and controlled disease seeding by a robotic ant will allow key predictions from network epidemiology to be tested, with broad implications for disease management strategies. I will then study the effect of colony size on social network structure and disease transmission, and how this in turn affects investment in personal immunity. This will shed light on far-reaching hypotheses about the effect of group size on social organisation ('size-complexity’ hypothesis) and immune investment (‘density-dependent prophylaxis’). Finally, I will explore whether prolonged pathogen pressure induces colonies to reinforce the transmission-inhibiting aspects of their social organisation (e.g., colony fragmentation) or to invest more in personal immunity. This project will represent the first empirical investigation of the role of social organisation in disease risk management, and allow its importance to be compared with other immune strategies. This will constitute a significant advance in our understanding of the complex feedback between sociality and health.

Mots-clés : social organisation,social network analysis,epidemiology,disease transmission,immune investment
Projet ERC : H-Unique (Horizon 2020, appel 2017, Advanced grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems Engineering
Entité participante : University of Dundee — Pays : Royaume-Uni — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 361668.48 €

H-unique will be the first multimodal automated interrogation of visible hand anatomy, through analysis and interpretation of human variation. It will be an interdisciplinary project, supported by anatomists, anthropologists, geneticists, bioinformaticians, image analysts and computer scientists. We will investigate inherent and acquired variation in search of uniqueness, as the hand retains and displays a multiplicity of anatomical variants formed by different aetiologies (genetics, development, environment, accident etc). Hard biometrics, such as fingerprints, are well understood and some soft biometrics are gaining traction within both biometric and forensic domains (e.g. superficial vein pattern, skin crease pattern, morphometry, scars, tattoos and pigmentation pattern). A combinatorial approach of soft and hard biometrics has not been previously attempted from images of the hand. We will pioneer the development of new methods that will release the full extent of variation locked within the visible anatomy of the human hand and reconstruct its discriminatory profile as a retro-engineered multimodal biometric. A significant step change is required in the science to both reliably and repeatably extract and compare anatomical information from large numbers of images especially when the hand is not in a standard position or when either the resolution or lighting in the image is not ideal. Large datasets are vital for this work to be legally admissible. Through citizen engagement with science, this research will collect images from over 5,000 participants, creating an active, open source, ground-truth dataset. It will examine and address the effects of variable image conditions on data extraction and will design algorithms that permit auto-pattern searching across large numbers of stored images of variable quality. This will provide a major novel breakthrough in the study of anatomical variation, with wide-ranging, interdisciplinary and transdisciplinary impact.

Mots-clés : anatomy,identification,biometric,automation