Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : EPIC (Horizon 2020, appel 2016, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
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 : 2159035.0 €

EPIC will automatically construct Evolutionary Program Improvement Collaborators (called Epi-Collaborators) that suggest code changes that improve software according to multiple functional and non-functional objectives. The Epi-Collaborator suggestions will include transplantation of code from a donor system to a host, grafting of entirely new features `grown' (evolved) by the Epi-Collaborator, and identification and optimisation of tuneable `deep' parameters (that were previously unexposed and therefore unexploited). A key feature of the EPIC approach is that all of these suggestions will be underpinned by automatically-constructed quantitative evidence that justifies, explains and documents improvements. EPIC aims to introduce a new way of developing software, as a collaboration between human and machine, exploiting the complementary strengths of each; the human has domain and contextual insights, while the machine has the ability to intelligently search large search spaces. The EPIC approach directly tackles the emergent challenges of multiplicity: optimising for multiple competing and conflicting objectives and platforms with multiple software versions. Keywords: Search Based Software Engineering (SBSE), Evolutionary Computing, Software Testing, Genetic Algorithms, Genetic Programming.

Mots-clés : search based software engineering  sbse,evolutionary computing,software testing
Projet ERC : ERIDIAN (Horizon 2020, appel 2015, Proof of concept grants)
Entité participante : Institute of Photonic Sciences — Pays : Espagne — Rôle : PI
Acronyme de l'entité : ICFO;ICFO-CERCA
Type d'entité : Recherche
Financement ERC de l'entité : 149925.0 €

ERIDIAN will develop a market-ready quantum random number generator for Device Independent (DI) Quantum Cryptography (QKD), which offers the best possible security guarantees. The prototype developed in ERIDIAN will enable major industrial players to make DI QKD a commercial reality. Current, classical cryptography is based on the assumed difficulty of certain mathematical problems and thus insecure against future mathematical advances and against quantum computers. Quantum cryptography gives future-proof security guaranteed by physical laws, but until now is very difficult to commercialise: the user must trust the communications equipment and its supplier, making a mass-market approach impossible. The DI approach to quantum cryptography uses strong, directly observable data correlations, technically a "loophole-free Bell Inequality Violation" (BIV), to guarantee security. Because the security (or not) of the connection is transparent to the user, there is no need to trust the communications provider, greatly facilitating commercialisation. The first loophole-free BIVs were demonstrated in 2015, using laboratory-grade random number generators developed in the ERC starting grant AQUMET. ERIDIAN will advance to the prototype stage this randomness generation technology, a critical element of the BIV and thus a requirement for the DI approach. The availability of a commercial-grade randomness source suitable for BIVs will allow industrial actors such as telecommunications providers to enter the DI field and offer solutions to a broad range of customers.

Mots-clés : quantum cryptography,device independent communications security,bell inequality violation,random number generation
Projet ERC : EUBorderCare (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : University of Bologna — Pays : Italie — Rôle : PI
Acronyme de l'entité : UNIBO
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 228256.9 €

EU Border Care is a comparative study of the politics of maternity care among undocumented migrants on the EU’s peripheries. Empirical analysis of personal and institutional relations of care and control in the context of pregnancy and childbirth will support an innovative critique of the moral rationale underpinning healthcare delivery and migration governance in some of Europe’s most densely crossed borderlands in France, Greece, Italy and Spain. Unlike other categories of migrants, undocumented pregnant women are a growing phenomenon, yet few social science or public health studies address EU migrant maternity care. This subject has urgent implications: whilst recent geopolitical events in North Africa and the Middle East have triggered a quantifiable increase in pregnant women entering the EU in an irregular situation, poor maternal health indicators among such women represent ethical and medical challenges to which frontline maternity services located in EU borderlands have to respond, often with little preparation or support from national and European central authorities. Grounded in long-term ethnographic fieldwork in maternity wards located in French Guiana and Mayotte (Overseas France), the North Aegean and Attica (Greece), Sicily (Italy), and Ceuta and Melilla (Spain), my project will trace the networks of maternity care delivery in peripheries facing an increase of immigration flows, and characterised by structural social and economic underinvestment. My team will investigate migrant maternity from three interlinked research perspectives: migrant women, healthcare delivery staff, and regional institutional agencies. Empirical and desk research, combined with creative audio-visual methods, will document migrant maternity on EU borderlands to address wider questions about identity and belonging, citizenship and sovereignty, and humanitarianism and universalism in Europe today.

Mots-clés : social and medical anthropology,migration,border studies,cultural pluralism,morality and ethics,humanitarian government,institutional care and welfare,european regionalism,kinship,personhood
Projet ERC : EXCITE (Horizon 2020, appel 2019, Proof of concept grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed matter physics
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 : 150000.0 €

EXCITE aims to deliver a key, scalable, industrial manufacturing process that will enable the production of excitonic devices for a wide range of applications. Our innovation can create a new market represented by excitonic devices using 2D materials as energy-efficient interconnects. The demonstration of room-temperature control could open the way to industrial applications of the excitonic device concept, with an excitonic switch and optical modulator being the first types of devices that could be implemented.

Mots-clés : optoelectronics,optical modulators,excitonic devices
Projet ERC : CellularBiographies (Horizon 2020, appel 2018, Advanced grants)
Domaine scientifique : Life Science
Panel : Cellular, Developmental and Regenerative Biology
Entité participante : University of Basel — Pays : Suisse — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2411440.0 €

Each cell in our body has a specific biography that is defined by its pedigree relationship with other cells (lineage) and by its history of gene expression (trajectory). A fundamental question in cellular and developmental biology has been how the lineage and trajectory of a cell lead to its specification and differentiation. Remarkable progress in genome editing and single-cell sequencing has generated the opportunity to understand this process at global scales and single-cell resolution. We have recently developed methods to reconstruct the cellular ancestry and transcriptional trajectories of cells during embryogenesis. The resulting lineage and trajectory trees can be analyzed to gain comprehensive views of how cellular diversity arises and how differentiation leads to physiologically specialized cell types. To generate such global views of cellular development, we will: 1. Define the cellular diversity and gene expression trajectories during zebrafish embryogenesis and organogenesis. Trajectory trees will be generated from scRNA-seq data and analyzed to reconstruct the gene expression pathways underlying fate specification. 2. Reveal the relationships between lineage and transcriptional trajectories during fate specification. Lineage trees will be generated by marking cells via genome editing and combined with trajectory trees to reveal the cellular paths towards fate specification. 3. Discover the gene expression cascades that remodel cells into physiologically functional types. Cell biological modules will be identified by comparing gene enrichment in differentiation trajectories and reveal the specialized and shared mechanisms of differentiation. These studies will help provide the first comprehensive and global view of the trajectories and lineages underlying vertebrate development. Our focus is on the zebrafish model system, but the data and concepts developed in this project will be applicable to other developmental and cellular systems.

Mots-clés : lineage,developmental biology of the cell,zebrafish
Projet ERC : MeChroRep (Horizon 2020, appel 2020, Advanced grants)
Domaine scientifique : Life Science
Panel : Molecules of Life: Biological Mechanisms, Structures and Functions
Entité participante : The Francis Crick Institute — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 1723514.0 €

Each time a human cell divides, it must make an exact copy of its 46 chromosomes. This requires precise duplication of ~6 billion base pairs of genomic DNA and all its associated proteins. Mistakes in this process can lead to developmental defects and cancer. Chromatin is packaged as nucleosomes comprising 147bp of DNA wrapped around a core of eight histone proteins. The nucleosome is a stable structure which must be disrupted each time the double helix is accessed during replication. ‘Epigenetic’ information in the form of covalent histone modifications must be re-established on both daughter strands immediately behind the replication fork. Inheritance of parental histones with their covalent marks must be coordinated with deposition of new histones into nucleosomes. And repressive heterochromatin must be replicated and immediately re-established after replication. Our goal is to achieve a deep understanding of how DNA replication is coordinated with the disassembly and reassembly of chromatin to ensure accurate chromosome duplication. We will use in vitro reconstitution of chromatin replication along with molecular genetics and structural biology to: • Generate a molecular view of how the budding yeast replication machinery, with other key factors, disrupts parental nucleosomes during replication. • Determine how parental nucleosomes are transferred to the nascent daughter strands. • Understand how deposition of new histones is coordinated with the inheritance of parental histones. • Characterise how budding yeast heterochromatin is disrupted and re-established during replication. • Begin studying this process using human proteins, focussing on areas where human replication mechanisms diverge from yeast. This work will underpin our long-term goal of reconstituting functional chromosomes from defined components to understand how DNA replication interacts with gene expression, DNA repair and chromosome segregation.

Mots-clés : chromatin replication
Projet ERC : NeuroMag (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Ludwig-Maximilians-Universität München — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : LMU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1061487.25 €

Each year millions of animals undertake remarkable migratory journeys, across oceans and through hemispheres, guided by the Earth’s magnetic field. While there is unequivocal behavioural evidence demonstrating the existence of the magnetic sense, it is the least understood of all sensory faculties. The biophysical, molecular, cellular, and neurological underpinnings of the sense remain opaque. In this application we aim to remedy this situation, exploiting an established assay, our unique infrastructure, and state-of-the-art methodology, using pigeons as a model system. The proposal will address three questions: 1) Where are the primary magnetosensors? 2) Where is magnetic information processed in the brain? 3) How is magnetic information encoded in the brain? In Aim 1 we will explore whether inner ear hair cells are the primary sensors, and if the detection of magnetic stimuli depends on the presence of magnetic crystals or electromagnetic induction. We will employ a range of physical methods to locate magnetite, and a molecular approach to identify putative electroreceptors. In Aim 2 we will use light sheet microscopy coupled with clearing methods to undertake whole brain mapping of magnetically-induced neuronal activation in the pigeon. We will complement these studies with transcriptomic methods to molecularly and anatomically define magnetosensitive circuits within the pigeon brain. We will build on this work in Aim 3 utilising in vivo 2-photon microscopy to investigate how cells within the pigeon brain encode magnetic information. We will determine whether neurons encode for specific components of the magnetic field (i.e. inclination, intensity, and polarity) and explore whether there are spatially restricted ensembles, providing a dynamic picture of magnetically induced neuronal activity. We anticipate that these experiments will reveal a secret that nature has kept hidden for millennia; How do animals detect magnetic fields?

Mots-clés : magnetoreception,pigeons,magnetite,electromagnetic induction,neuronal circuits,hippocampus
Projet ERC : NaturalPhilosophy (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Cultures and cultural production
Entité participante : University of Groningen — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : RUG
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1480611.0 €

Early modern natural philosophy underwent dramatic transformations that completely reshaped its conceptual framework and set of practices. The main contention of my ERC project is that teaching practices had a decisive and ‘normalising’ impact on the progressive dissemination, adaptation and selection of rival conceptions of natural philosophy. Normalisation occurs when historical actors collectively present certain tenets as crucial for the study of a discipline, and thus prescribe them as a necessary subject for teaching and learning. The overall aim of this ERC project is to determine and explain how the process of normalisation embedded in teaching practices shaped the evolution of early modern natural philosophy. To study normalisation, it is necessary to operate a systematic comparative investigation of hundreds of works through which natural philosophy was taught, learned and reshaped, both within and outside universities. The size of this corpus defies the traditional method of close reading used by historians of philosophy and science. I will meet this challenge by organically integrating close reading with digital ‘distant reading’. I will digitally transcribe a corpus of approximately 500 early modern works on natural philosophy, published in Britain, France and the Dutch Republic. Using digital tools to investigate how the networks of authors and concepts of natural philosophy co-evolved over time will allow me to identify textual excerpts that are representative of historical trends. By analysing these excerpts with close reading and assessing them against the digital results, I will determine and explain how normalisation shaped the evolution of natural philosophy. This project will boost the integration of digital approaches in the history of philosophy and science by producing a newly digitised corpus, tools customized for analysing early modern texts, and methodological reflections on their implementation.

Mots-clés : natural philosophy,normalisation,distant reading,history of early modern philosophy and science,history of universities,digital humanities
Projet ERC : ECOWORM (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Life Science
Panel : Environmental Biology, Ecology and Evolution
Entité participante : Leipzig University — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1498620.0 €

Earth is experiencing substantial biodiversity losses at the global scale, while both species gains and losses are occurring locally and regionally. Nonrandom changes in species distributions could profoundly influence ecosystem functions and services. However, few experimental tests have examined the influences of invasive ecosystem engineers, which can have disproportionally strong impacts on native ecosystems. Invasive earthworms are a prime example of ecosystem engineers that influence many ecosystems around the world. In particular, European earthworms invading northern North American forests may cause simultaneous species gains and losses with significant consequences for essential ecosystem processes like nutrient cycling and crucial services like carbon sequestration. Using a synthetic combination of field observations, field experiments, lab experiments, and meta-analyses, the proposed work will be the first systematic examination of earthworm effects on relationships between plant communities, soil food webs, and ecosystem processes. Further, effects of a changing climate on the spread and consequences of earthworm invasion will be investigated. Meta-analyses will be used to test if earthworms cause invasion waves, invasion meltdowns, habitat homogenization, and ecosystem state shifts. Global data will be synthesized to test if the relative magnitude of effects differ from place to place depending on the functional dissimilarity between native soil fauna and exotic earthworms. Moving from local to global scale, the present proposal examines the influence of earthworm invasions on biodiversity–ecosystem functioning relationships from an aboveground–belowground perspective. This approach is highly innovative as it utilizes exotic earthworms as an exciting model system that links invasion biology with trait-based community ecology, global change research, and ecosystem ecology, pioneering a new generation of biodiversity–ecosystem function research.

Mots-clés : aboveground belowground interactions,biodiversity ecosystem functioning,earthworm invasion
Projet ERC : USMILE (Horizon 2020, appel 2019, Synergy grants)
Entité participante : University of Valencia — Pays : Espagne — Rôle : PI
Acronyme de l'entité : UV
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2038013.0 €

Earth system models are fundamental to understand climate change. Although they have improved significantly, considerable biases and uncertainties in their projections remain. Process parameterisations limit the models’ ability to simulate both global and regional Earth system responses, which are key for assessing climate change and its impacts on ecosystems and society. In recent years, the volume of data from high-resolution models and observations has substantially increased to petabyte scales. Concomitantly, the field of machine learning (ML) has quickly developed, promising breakthroughs in detecting and analysing non-linear relationships and patterns in large multivariate datasets. Yet, traditionally, physical modelling and ML have been often treated as two different worlds with opposite scientific paradigms (theory-driven versus data-driven). Thus, despite its great potential, ML has not yet been widely adopted for addressing the urgent need of improved understanding and modelling of the Earth system. USMILE will combine multi-disciplinary expertise in ML and process-based atmosphere and land modelling to completely rethink model development and evaluation. ML will further allow us to define novel observational constraints on Earth system feedbacks and climate projections. We will (1) develop ML algorithms to enhance Earth observation datasets accounting for spatio-temporal covariations, (2) deploy ML-based parameterisations and sub-models for clouds and land-surface processes that have hindered progress in climate modelling for decades, and (3) detect and understand modes of climate variability, multivariate extremes and uncover dynamical aspects of the Earth system with novel deep learning and causal inference techniques. USMILE will drive a paradigm shift in the current modelling of the Earth system towards a new data-driven physics-aware science and to an unprecedented reduction of uncertainties in projections.

Mots-clés : earth system modelling,climate projections,climate sensitivity,earth system feedbacks,earth system model evaluation,causal inference,anomaly detection,deep learning,recurrent neural networks,gaussian processes
Projet ERC : USMILE (Horizon 2020, appel 2019, Synergy grants)
Entité participante : German Aerospace Center — Pays : Allemagne — Rôle : CO-PI
Acronyme de l'entité : DLR
Type d'entité : Recherche
Financement ERC de l'entité : 2977864.0 €

Earth system models are fundamental to understand climate change. Although they have improved significantly, considerable biases and uncertainties in their projections remain. Process parameterisations limit the models’ ability to simulate both global and regional Earth system responses, which are key for assessing climate change and its impacts on ecosystems and society. In recent years, the volume of data from high-resolution models and observations has substantially increased to petabyte scales. Concomitantly, the field of machine learning (ML) has quickly developed, promising breakthroughs in detecting and analysing non-linear relationships and patterns in large multivariate datasets. Yet, traditionally, physical modelling and ML have been often treated as two different worlds with opposite scientific paradigms (theory-driven versus data-driven). Thus, despite its great potential, ML has not yet been widely adopted for addressing the urgent need of improved understanding and modelling of the Earth system. USMILE will combine multi-disciplinary expertise in ML and process-based atmosphere and land modelling to completely rethink model development and evaluation. ML will further allow us to define novel observational constraints on Earth system feedbacks and climate projections. We will (1) develop ML algorithms to enhance Earth observation datasets accounting for spatio-temporal covariations, (2) deploy ML-based parameterisations and sub-models for clouds and land-surface processes that have hindered progress in climate modelling for decades, and (3) detect and understand modes of climate variability, multivariate extremes and uncover dynamical aspects of the Earth system with novel deep learning and causal inference techniques. USMILE will drive a paradigm shift in the current modelling of the Earth system towards a new data-driven physics-aware science and to an unprecedented reduction of uncertainties in projections.

Mots-clés : earth system modelling,climate projections,climate sensitivity,earth system feedbacks,earth system model evaluation,causal inference,anomaly detection,deep learning,recurrent neural networks,gaussian processes