Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : LAWALISI (Horizon 2020, appel 2015, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Cultures and cultural production
Entité participante : University of Exeter — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2212638.6 €

The academic study of Islamic law has, so far, almost exclusively focused on Sunni legal thought. The legal thought and practice of Shi’ite (and other) traditions has been neglected, and this has created a rather skewed account of the history of Islamic law. This project aims to rectify this inadequacy by producing a body of research in which the Imami Shi’ite contribution to Islamic legal history is described, analysed and evaluated. Imami Shi’ites, sometimes termed Twelvers, are the largest branch of Shi’ism today. Imamis form a majority in Iran and Iraq where the major Shi’i centres of legal learning are located. In the project, we aim to examine the theories and methods used by scholars in the study of Islamic law, derived mainly from Sunni sources, and test them against the Shi’ite legal literature. The project aims to demonstrate that a non-Sunni tradition of Islamic legal thought, in this case Imami Shi’i law, can illuminate and enrich the general history of Islamic law. At times, Shi'ite law shares features with other legal schools; at other times it provides an alternative account, challenging long held assumptions concerning Islam’s legal development. The project will do this through 5 independent, but linked, Research Themes, in which research fellows and visiting professors will carry out detailed programmes of research. These will cover Imami law and doctrine, the dynamics of legal authority, the relationship between legal theory and doctrine and the influence of law on political theory. The project will facilitate opportunities to test the researchers' research findings with both international experts in the field, and scholars from within the Imami legal tradition. The Principal Investigator, Robert Gleave, has made a major contribution to this area in his research, publications and other activities for 20 years, and this project extends and expands this interest, aiming to make a lasting impact on the field of Islamic legal studies in the future.
Projet ERC : Tesseract (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : Barcelona Supercomputing Center — Pays : Espagne — Rôle : PI
Acronyme de l'entité : BSC
Type d'entité : Recherche
Financement ERC de l'entité : 1478135.0 €

The accuracy obtained in wind tunnel aerodynamic and aero-acoustic measurements is extremely demanding and it still challenges our current simulation technologies. It mainly challenges the capabilities of current mesh generation technologies used in flow simulations. The ground-breaking TESSERACT project addresses the challenge of studying how to generate computational meshes that enable the ability to obtain computer flow simulations that beat the predictive capabilities of the wind tunnel experiments for a fixed accuracy, cost, and time scale. These important challenges correspond to capabilities that have been considered essential to fulfil the European strategic goals of future transportation. The main objective is to generate optimal quality curved adapted meshes for space-time flow simulations by addressing the following ambitious and beyond the state of the art 4-dimensional meshing research objectives: curved geometry representation and approximation, mesh quality measures, adapted mesh resolution, and space-time flow simulation. This is a high risk project since it tackles meshing objectives in 4D while lower dimension versions of these issues have not yet been fully solved. However, providing the foundations and the methods to improve current space-time meshing algorithms will suppose a high gain in the field of computational and aerospace engineering. This is so since in the near future, it will be of major importance to conduct accurate, robust, and efficient parallel in space-time adapted flow simulations that exploit the computational power of the exascale super-computing facilities to come. To enhance the feasibility of the project, the scientific approach considers different novel approaches to reach the same objectives and therefore, bear in mind the high-risk / high-gain nature of this 4D meshing project.

Mots-clés : computational fluid dynamics,mesh generation,unstructured high order methods,curved meshing,mesh quality measures,anisotropic meshing,hybridizable discontinuous galerkin method
Projet ERC : MuFLOART (Horizon 2020, appel 2018, Proof of concept 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 : 149249.0 €

The accurate quantification of microbial populations is critical for understanding how microbes evolve and adapt to stressful environments. This, in turn, can help improve rationales for drug deployment aimed at minimising the evolution of antibiotic resistance. Working on ERC-funded research into how microbial communities optimise their virulence traits and mediate drug-resistance, our team identified a need for a low cost photometry device capable of high-throughput quantification of microbial phenotypes such as microbial growth rates and metabolic efficiencies; microbial dose responses and susceptibility to antibiotics; antibiotic interactions datasets; microbial individuality and heterogeneity data and population mean gene expression profiles for fluorescently labeled genes. There are multiple positive consequences of having these capabilities at low cost: we can create high-throughput data pipelines for microbiology research, particularly for evolutionary studies or phenotypic screens; we can improve the dissemination of high-quality phenotypic data analysis algorithms (such as synergy tests for antibiotics) and so provide standardisation for those phenotypic assays; schools can benefit from the low-costs laboratory hardware and bring data analysis for microbiology into their mathematics, biology or computer science teaching; our lightweight devices with small footprints can be used to access geographically hard-to-reach areas where drug resistance phenotyping could be conducted using either battery or solar power. We are, therefore, now in the process of creating two prototypes that can perform these functions and we are seeking funding to finalise their design and field test the devices in three different settings: a partner research laboratory studying microbial evolution, a hospital research laboratory studying antimicrobial resistance and finally schools in a top-10 deprived area of the UK that cannot afford biotechnology equipment for teaching science.

Mots-clés : antibiotic resistance,low cost spectrophotometry,microbial evolution,optimisation algorithms,high throughput data analysis,science education,microbiology research
Projet ERC : SUPERCELL (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : Spanish National Research Council — Pays : Espagne — Rôle : PI
Acronyme de l'entité : CSIC
Type d'entité : Recherche
Financement ERC de l'entité : 1920738.0 €

The advances in paper microfluidics taking place in recent years show that the new generation of paper-based devices will be able to overcome the limitations of traditional lateral flow tests and offer more accurate and specific information. However, the quantification of paper devices signals by colorimetry, electrochemistry or fluorescence entails the use of sensors and electronic components that require energy to function. Up to now, this has been solved by the use of battery-powered bulky readers, but this is only cost-effective when the reader is meant to be used thousands of times (hospital, care rooms, etc). It is becoming clear that the proliferation of paper-based sensors and their enormous potential impact when applied to personalized healthcare ask for innovative solutions that provide affordable readout. Despite dedicated on-chip solutions that integrate all the required components within a disposable test seem to be the most promising approach, available examples of realization are still scarce. The SUPERCELL project aims to develop a new generation of disposable and low environmental impact fuel cells. The approach presented in this proposal will be a major breakthrough in the fuel cell field, as these devices are conceived for the first time as single-use and disposable power sources. It will also have an enormous impact in the point-of-care diagnostics domain, as it will provide simple, reliable and clean power sources to an upcoming generation of smart paper-based sensors and allow them to be energetically autonomous. Fuel will be harvested from the biological sample to be analyzed – in case of urine and blood – or taken from hydrogen produced in situ upon the addition of any liquid in the paper platform. The proposal is very innovative in conception as well as in technology as these devices will be developed by means of a smart integration of paper microfluidics, printed electronics and electrocatalysis technologies.

Mots-clés : microfuel cells,paper microfluidics,organic electronics
Projet ERC : FeREDCOUPLS (Horizon 2020, appel 2015, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Universität Hamburg — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : UHH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1645184.48 €

The aerobic conditions on our planet enable the accumulation of oxidized matter whereas reduced chemicals are the most valuable energy carriers. Future shortages of energy-rich resources make efficient reductive transformations one of the greatest scientific challenges. To address this societal, economic and environmental demand, we propose new approaches to the design and application of stabilized iron catalysts. Our endeavour exploits the higher reducing power of Fe (vs. noble metals) in challenging reductive transformations and capitalizes on the high sustainability of Fe catalysis over noble metal technologies. The use of low-valent Fe catalysts, the realization of new catalytic reactions and their mechanistic understanding will only be possible through the controlled generation and effective stabilization of reduced Fe species and active nanoparticles. Major emphasis will be placed on coordinative ligand/solvent systems which accommodate electron-rich Fe centers (olefins, arenes, Lewis acids, redox-ligands, ionic liquids). We address new approaches to the synthesis of low-valent Fe complexes and bottom-up/top-down preparations of Fe(0) nanoparticles. Catalytic reactions of high relevance to the manufacture of chemicals and materials will be studied (reduction, cross-coupling, hydrogenation, defunctionalization) with special emphasis on cheap abundant substrates. Mechanistic studies aim at understanding Fe-centered reductive bond activations and ligand co-operation. The proposed use of the most abundant transition metal for challenging reductive processes under practical conditions extends beyond the realm of synthesis, catalysis, and materials into spectroscopy, solvent technologies and reaction processing with direct relevance to sustainable chemicals and energy production. Our multidisciplinary program will provide new sets of active iron catalysts for reductive processes and is a major puzzle piece toward a greener chemical synthesis.
Projet ERC : Elephant Project (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Life Science
Panel : Environmental Biology, Ecology and Evolution
Entité participante : University of Sheffield — Pays : Royaume-Uni — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 0.0 €

The ageing population structure of most European countries has major health, economic and social consequences that lead to a need to better understand both the evolutionary limitations of deferring ageing, as well as the mechanisms involved in growing old. Ageing involves reduced fertility, mobility and ability to combat disease, but some individuals cope with growing old better than others. Improving the quality of life at old age and predicting future changes in longevity patterns of societies might depend on our ability to develop indicators of how old we really are and how many healthy years we have ahead, and how those indicators depend on our health history across several decades. Yet, most model species used in biology are short-lived and provide a poor comparison to long-lived mammals such as humans. Further, they do not often inform on the mechanisms of ageing alongside its fitness consequences in natural populations of long-lived mammals. This project integrates different ageing mechanisms with unique data on lifelong disease and reproductive history in the most long-lived non-human mammal studied so far, the Asian elephant. I will examine how different mechanisms of ageing (telomere dynamics, oxidative stress and telomerase activity) interact with lifelong disease and reproductive history, and current endocrinological measures of stress and reproductive status. This will help us to better understand both the mechanisms of ageing and their consequences on senescence rates. To do so, I will combine the most comprehensive demographic data (N~10.000) on Asian elephants in the world with bi-monthly health assessments and disease records across life (N~2500) and with longitudinal markers of ageing and hormonal correlates of stress and reproductive potential (N~240). Understanding changes in health across life and its links to ageing rates, stress levels and life-history in a species as long-lived as humans will be relevant to a large range of end-users.

Mots-clés : aging,senescence,lifespan,asian elephant,behavioural ecology,life history theory,reproductive success,fitness,natural selection,stress level,mammal,sex difference,health
Projet ERC : BIAF (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : University of Bristol — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1998546.0 €

The agile and efficient flight of birds shows what flight performance is physically possible, and in theory could be achieved by unmanned air vehicles (UAVs) of the same size. The overall aim of this project is to enhance the performance of small scale UAVs by developing novel technologies inspired by understanding how birds are adapted to interact with airflows. Small UAVs have the potential to dramatically change current practices in many areas such as, search and rescue, surveillance, and environmental monitoring. Currently the utility of these systems is limited by their operational endurance and their inability to operate in strong turbulent winds, especially those that often occur in urban environments. Birds are adapted to be able to fly in these conditions and actually use them to their advantage to minimise their energy output. This project is composed of three tracks which contain elements of technology development, as well as scientific investigation looking at bird flight behaviour and aerodynamics. The first track looks at developing path planning algorithms for UAVs in urban environments based on how birds fly in these areas, by using GPS tracking and computational fluid dynamics alongside trajectory optimization. The second track aims to develop artificial wings with improved gust tolerance inspired by the features of feathered wings. Here, high speed video measurements of birds flying through gusts will be used alongside wind tunnel testing of artificial wings to discover what features of a bird’s wing help to alleviate gusts. The third track develops novel force and flow sensor arrays for autonomous flight control based on the sensor arrays found in flying animals. These arrays will be used to make UAVs with increased agility and robustness. This unique bird inspired approach uses biology to show what is possible, and engineering to find the features that enable this performance and develop them into functional technologies.

Mots-clés : unmanned air vehicles,bio inspired,aerodyamics,path planning,flight control,bird flight
Projet ERC : M-Runners (Horizon 2020, appel 2018, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : German Aerospace Center — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : DLR
Type d'entité : Recherche
Financement ERC de l'entité : 1860855.0 €

The aim of M-Runners is to thoroughly advance the understanding of fundamental dynamic principles of legged locomotion to the point that those principles can be used to design robots which display similar motion characteristics, versatility, and efficiency as their biological paragons. The central hypothesis of the project is that biological locomotion is fundamentally determined by the mechanical resonance properties of the body and that a breakthrough in robot locomotion is essentially linked to understanding and exploiting these phenomena. If body design is such that walking and running correspond to intrinsic periodic motions of the body, then the control is simple and efficiency and robustness are natural consequences. However, large-amplitude nonlinear oscillations of such complex systems are today still not well understood. Mathematical methods to describe, analyze, design and control elastic resonant robots are lacking to a large extent. The project is thus dedicated to develop a new theory of nonlinear oscillations, applicable to elastic multibody systems, be they biologic or robotic. M-Runners will perform interdisciplinary research at the border between robotics, nonlinear dynamical systems and vibration theory, biomechanics, and machine learning. We will take inspiration from biology regarding the basic motion sequences and the muscle arrangements (couplings, redundancies, compliance distributions). Conversely, we expect our theory to generate new hypotheses for a deeper understanding of locomotion biomechanics and its control by the nervous system. We will design and demonstrate robots which can move at similar speed and mechanical energetic efficiency as animals and humans and which have comparable uneven terrain versatility and robustness. The primary application scenario is space exploration on Mars in canyons, caves or steep ridge slopes. Applications of the technology reach, however, from health-care over personal-assistance to disaster management.

Mots-clés : bio mechanics,mechatronics,system dynamics
Projet ERC : SUSTAIN (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The study of the human past
Entité participante : Leibniz Association — Pays : Allemagne — Rôle : Partner
Acronyme de l'entité : LG
Type d'entité : Recherche
Financement ERC de l'entité : 177250.0 €

The aim of SUSTAIN is to elucidate how early agricultural societies in Europe maintained their capacity to self-generate and persist through time. It examines societies that have only recently settled in new environments and begun to practice mixed agro-pastoral husbandry. This at the time novel way of interaction with the environment involved a different scale of interference with the life cycles of plants and animals, and was of fundamental importance for subsequent developments in Europe. While archaeological research traditionally centres on the question of how societies and livelihoods changed, the project asks what made them capable to carry on, i.e. to remain sustainable. The project weaves together approaches from social anthropology, biogeography, bioarchaeology and complexity science in a manner that has not been attempted previously. We will examine the relationship of domesticates’ past range expansions and climate using correlative species distribution models (SDMs) and will derive past agropastoral practices from isotope signals in plant and animal remains. Novel approaches of quantitative inference will allow us to estimate parameters of crop and livestock productivity and to directly compare agropastoral performance across time and space in Europe. Using this knowledge, we will employ spatially explicit agent-based simulation to explore the social parameters of growth and decline of the first agricultural societies in Europe in previously unconsidered detail. We will examine the role of social webs of lateral insurance in colonising populations, and the extent to which fragmentation of these relationships constrained population expansion into new regions. The project will produce insights that challenge conventional wisdom about farming and environmental sustainability, particularly in regard to social connectedness, relations of sharing, and mobility, which will have value and impact beyond archaeology.

Mots-clés : stable isotope analysis,agent based modelling,bioarchaeology,biomolecular archaeology,model based archaeology,neolithic archaeology,computational social science,sustainability science,environmental humanities
Projet ERC : UNION (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2311847.0 €

The aim of UNION is to liberate machine learning, enabling everyone to use it productively and creatively instead of being the demesne of experts. Today, machines must be explicitly taught any new skill via manual supervision, incurring a cost justifiable only for applications of general interest. Thus, as laypeople, we can benefit from powerful search engines, medical devices and personal assistants that are designed by means of machine learning, but we cannot easily teach machines to address our particular professional or personal needs. From recognizing illustrations on Greek vases to building catalogues of store products, machine learning could empower millions of individuals, but current technology cannot scale to these micro-tasks. The goal of UNION is thus to develop machines that can learn to understand audio-visual data with little to no manual supervision, opening up artificial intelligence to countless new applications. To this end, UNION will investigate two key hypotheses. The first is that concepts that reflect intrinsic properties of the natural world, such as detachable objects and their 3D geometry, physics and high-level class, can be learned without manual supervision, while still being interpretable to a human. The second hypothesis is that, given this ability, a machine can pick up new skills useful to specific stakeholders from no or just a few manually-annotated examples. These hypotheses will be validated (1) by developing algorithms that can learn without manual supervision, (2) by endowing machines with advanced general-purpose audio-visual analytical skills, and (3) by using the knowledge already acquired to learn new skills very efficiently, from little data and even less manual supervision. This will be delivered as an open-source package that will demonstrate how one can create open-ended audio-visual analysis software that can be taught a large variety of different tasks with at most lightweight manual assistance.

Mots-clés : image understanding,unsupervised learning,visual geometry