Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : PortPath (Horizon 2020, appel 2020, Proof of concept grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : Technische Universität Berlin — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : TUB
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Many of today’s water resources are contaminated by pathogenic bacteria, which cause serious diseases, especially in low-income countries. One particularly grave concern is Vibrio cholerae, the disease-causing agent of cholera, with an estimated 1.3 to 4.0 million cases worldwide every year and an estimated annual mortality rate of up to 143,000 people. The current state-of-the-art detection methods for this pathogen have high demands on the equipment and on the expertise of the investigating person. In addition, a positive detection generally takes several hours and is very expensive. PortPath presents a completely new approach for the detection of pathogens (with emphasis on the cholera pathogen), which is based on automated analysis of water samples contaminated with pathogens. PortPath is not only much faster and cheaper than previous detection methods but can also be used by non-medical laypersons. An easy-to-use software together with low-cost hardware shows whether pathogens are present in a water sample. The software uses the recent developments in computer technology, especially in machine learning strategies. Significant components of PortPath have already been developed. The two objectives during the Proof of Concept phase are (1) the technical validation with a first prototype of PortPath, and (2) the development of a business plan for PortPath.

Mots-clés : pathogen,detection,tracking,motility,microbe,water,smartphone,magnification,algorithm,health,machine learning
Projet ERC : DiaDev (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Cultures and cultural production
Entité participante : Public Health Foundation of India — Pays : Inde — Rôle : Partner
Acronyme de l'entité : PHFI
Type d'entité : Recherche
Financement ERC de l'entité : 0.0 €

Many people living in resource-poor settings have never had access to the laboratory facilities or technical expertise that are needed to diagnose diseases. With the development and deployment of a new generation of affordable, easy-to-use, and portable diagnostic devices that are designed for places with no laboratory infrastructure, the global landscape of diagnosis is dramatically changing. Can portable diagnostic devices strengthen health systems in resource-poor settings? DiaDev is the first study to comprehensively address this question through comparison across multiple devices, sites of production, and contexts of use. Diagnostic technologies are often championed as universal solutions to health equity and access. This ethnographic study investigates the extent to which how diagnostic devices work and what they can achieve depends on the locally specific relationships through which they are designed and used. Five ethnographic case studies from the USA, India and Sierra Leone examine the partnerships between donors, governments, humanitarian organisations and users that characterise current models of technological innovation and implementation in global health, the processes by which diagnostic devices are integrated with health infrastructures in target settings, and the practices of knowledge production and use they entail. The ultimate aim is to generate new insight into (i) changing relationships of power between governments, donors, and business in global health, and (ii) the ways in which diagnostic devices are transforming health systems in resource-poor settings. It will produce a new context-specific and comparative framework for exploring the opportunities and challenges involved in the design and use of diagnostic devices. Through innovative collaborative methods it will also develop a set of ‘health system strengthening tools’ that will enable stakeholders to improve the design and use of these devices in dialogue with the project findings.

Mots-clés : global health,institutions,partnership,value,humanitarian enterprise
Projet ERC : BiocatSusChem (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : University of Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1997286.0 €

Many significant global challenges in catalysis for energy and sustainable chemistry have already been solved in nature. Metalloenzymes within microorganisms catalyse the transformation of carbon dioxide into simple carbon building blocks or fuels, the reduction of dinitrogen to ammonia under ambient conditions and the production and utilisation of dihydrogen. Catalytic sites for these reactions are necessarily based on metals that are abundant in the environment, including iron, nickel and molybdenum. However, attempts to generate biomimetic catalysts have largely failed to reproduce the high activity, stability and selectivity of enzymes. Proton and electron transfer and substrate binding are all finely choreographed, and we do not yet understand how this is achieved. This project develops a suite of new experimental infrared (IR) spectroscopy tools to probe and understand mechanisms of redox metalloenzymes in situ during electrochemically-controlled steady state turnover, and during electron-transfer-triggered transient studies. The ability of IR spectroscopy to report on the nature and strength of chemical bonds makes it ideally suited to follow the activation and transformation of small molecule reactants at metalloenzyme catalytic sites, binding of inhibitors, and protonation of specific sites. By extending to the far-IR, or introducing mid-IR-active probe amino acids, redox and structural changes in biological electron relay chains also become accessible. Taking as models the enzymes nitrogenase, hydrogenase, carbon monoxide dehydrogenase and formate dehydrogenase, the project sets out to establish a unified understanding of central concepts in small molecule activation in biology. It will reveal precise ways in which chemical events are coordinated inside complex multicentre metalloenzymes, propelling a new generation of bio-inspired catalysts and uncovering new chemistry of enzymes.

Mots-clés : infrared spectroscopy,metalloenzymes,bioelectrochemistry,proton coupled electron transfer
Projet ERC : CRYSTALEYES (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Ben-Gurion University of the Negev — Pays : Israël — Rôle : PI
Acronyme de l'entité : BGU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1966000.0 €

Many spectacular optical phenomena in nature are produced by the interaction of light with organic crystals. Organisms exert exquisite control over the habit and organization of these crystals to determine the type of optical effect produced by using strategies beyond the state of the art in solid state chemistry. Despite their important role in animal behavior and their huge potential to inspire new optical materials, little is known about these materials. However, recent discoveries of previously unknown organic bio-crystals indicate that many more of these materials will be found and that ‘organic bio-crystallization’ is an emergent field with important implications for materials science. My overall objective is to uncover the strategies organisms use to control the formation of organic crystals, enabling these strategies to be harnessed to develop new crystalline organic materials. A pioneering approach is proposed which entails following the crystallization pathways of organic molecules in model photonic systems undergoing development. The crystallization of guanine and isoxanthopterin will be investigated to reveal the physio-chemical and biological processes underpinning crystallization. Cryogenic electron microscopy, spectroscopy and in situ diffraction methods will determine changes in the chemical and physical properties of the crystals during crystallization. Proteomic and transcriptomic studies will identify the macromolecules responsible for controlling nucleation and growth and the genes encoding them. These bio-crystallization processes will then be artificially mimicked and manipulated to produce guanine and isoxanthopterin crystals with rationally designed crystal properties (crystal habit, composition, size), including an ambitious attempt to genetically programme guanine-producing iridophore cells as living factories to produce crystals with controlled phenotypes, laying the foundations for a new field of genetically programmed organic materials.

Mots-clés : bio crystallization,biomineralization,organic molecular crystals,optical materials
Projet ERC : MSTAR (Horizon 2020, appel 2017, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : Chalmers University of Technology — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2500000.0 €

Massive stars are important throughout astrophysics, yet there remain many open questions about how they form. These include: What is the accretion mechanism of massive star formation? What sets the initial mass function of stars, especially at the highest masses? What is the relation of massive star formation to star cluster formation? How do massive star and star cluster formation vary with galactic environment? What was the nature of the first stars to form in the universe and could these have been the seeds for supermassive black holes? With recent advances in both theoretical/computational techniques and observational facilities, the time is now ripe for progress on answering these questions. Here we propose an ambitious research program that combines latest theoretical studies of massive star and star cluster formation, including analytic, semi-analytic and full numerical simulations, with state-of-the-art observational programs, including several large surveys. We will: 1) Develop new theoretical models for how individual massive stars form from gas cores, focusing on diagnostics and including study of how the process depends on galactic environment; 2) Test these protostar models against observations, especially with ALMA, SOFIA, JVLA, HST and in the near future with JWST and eventually TMT & E-ELT; 3) Develop theoretical models for star cluster formation, including both magneto-hydrodynamics of the gas and N-body modeling of the young stellar population, with the focus on how massive stars form and evolve in these systems; 4) Test these protocluster models against observational data of young and still-forming star clusters, especially with ALMA, HST, Chandra, JWST and ground-based near-IR facilities; 5) Explore new theoretical models of how the first stars formed, with potential implications for the origins of supermassive black holes - one of the key unsolved problems in astrophysics.

Mots-clés : massive star formation,star cluster formation,supermassive black hole formation,theoretical modeling  e g,astrochemistry,protostellar evolution,observational astronomy  x ray to sub mm
Projet ERC : ASSESS (Horizon 2020, appel 2017, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : National Observatory of Athens — Pays : Grèce — Rôle : PI
Acronyme de l'entité : NOA
Type d'entité : Recherche
Financement ERC de l'entité : 1128750.0 €

Massive stars dominate their surroundings during their short lifetimes, while their explosive deaths impact the chemical evolution and spatial cohesion of their hosts. After birth, their evolution is largely dictated by their ability to remove layers of hydrogen from their envelopes. Multiple lines of evidence are pointing to violent, episodic mass-loss events being responsible for removing a large part of the massive stellar envelope, especially in low-metallicity galaxies. Episodic mass loss, however, is not understood theoretically, neither accounted for in state-of-the-art models of stellar evolution, which has far-reaching consequences for many areas of astronomy. We aim to determine whether episodic mass loss is a dominant process in the evolution of the most massive stars by conducting the first extensive, multi-wavelength survey of evolved massive stars in the nearby Universe. The project hinges on the fact that mass-losing stars form dust and are bright in the mid-infrared. We plan to (i) derive physical parameters of a large sample of dusty, evolved targets and estimate the amount of ejected mass, (ii) constrain evolutionary models, (iii) quantify the duration and frequency of episodic mass loss as a function of metallicity. The approach involves applying machine-learning algorithms to existing multi-band and time-series photometry of luminous sources in ~25 nearby galaxies. Dusty, luminous evolved massive stars will thus be automatically classified and follow-up spectroscopy will be obtained for selected targets. Atmospheric and SED modeling will yield parameters and estimates of time-dependent mass loss for ~1000 luminous stars. The emerging trend for the ubiquity of episodic mass loss, if confirmed, will be key to understanding the explosive early Universe and will have profound consequences for low-metallicity stars, reionization, and the chemical evolution of galaxies.

Mots-clés : massive stars,mass loss,stellar evolution,variability
Projet ERC : ADAM (Horizon 2020, appel 2019, Synergy grants)
Entité participante : University of Southampton — Pays : Royaume-Uni — Rôle : CO-PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 3493039.0 €

Materials impact most aspects of our lives, including healthcare, energy production, data storage and pollution control. However, the design of functional materials cannot be approached with the certainty and the engineering rules that would be used in planning and constructing a macroscopic object, such as a car or bridge. This is because of the limited scope for design that exists at the atomic scale: experimentally realizable materials must correspond to local minima on a complex, multidimensional energy surface, whose positions and depths are difficult to predict. This project will change the way that we discover new molecular materials by revolutionizing the exploration process, rather than focussing on rules for intuitive design. This will be achieved through a unique synergistic partnership between three principal investigators, bringing together an international leader in crystal structure modelling and prediction methods, an experimental chemist with a track record for inventing new classes of functional materials, and a pioneer in robotics for laboratory and process automation. The programme integrates state-of-the-art computation, experiment and robotics, building on joint breakthroughs from our team (Nature, 2011; Nature, 2017) that lay the groundwork for a transformation in our materials discovery capabilities. We will build a Computational Engine for evolutionary exploration of chemical space using crystal structure prediction and machine learning of structure-property relationships for the assessment of molecules. In parallel, we will develop an Experimental Engine for autonomous synthesis and properties testing using newly-developed, artificially-intelligent, mobile ‘robot chemists’. The vision of ADAM is to couple these two engines together, creating an autonomous discovery platform that amplifies human creativity by searching the vast, unexplored chemical space for new materials with step change properties.

Mots-clés : materials discovery,chemistry automation
Projet ERC : Apollo (Horizon 2020, appel 2019, Proof of concept grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Social World and Its Diversity
Entité participante : University of Leeds — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Mathematical models of human decision making are a widely used tool for advising policy makers and industry by making predictions of future demand for products and services. The reliability of the predictions depends on the robustness of the underlying mathematical models. A very active field of academic research is concerned with the refinement of existing model structures and the development of new approaches. Over the last two decades, fuelled by the availability of ever more powerful computing resources, there has been a major step change in the mathematical complexity of these models. However, the vast majority of real world users of choice models, and also many academic users, lack a programming background. This means that most users are restricted to those models which are covered in existing software, and models developed by analysts who lack programming skills fail to be used in practice. A core output from the ERC-CoG grant DECISIONS (615596) has been the development of the Apollo package. Apollo is a powerful open source solution for the estimation and application of choice models. The current PoC proposal seeks to fully explore the innovative research that led to the development of Apollo and to take the first steps in establishing Apollo as a next generation tool for choice modelling, with full customisation possibilities including for inexperienced users. We propose to make changes to the existing implementation of Apollo to provide users with an easy to use template for developing code and to create a system for testing user-developed functions for new models, standardise the code used in them, and incorporate them in releases of new versions of Apollo to make them available to other users. In addition, we propose to introduce a pay-to-program service where users can pay to have new features developed. These features will be released to all users after an embargo period during which they are limited to the user who paid for their development.

Mots-clés : choice modelling,software,freeware
Projet ERC : MAJORIS (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : Institut national de recherche en sciences et technologies du numérique — Pays : France — Rôle : PI
Acronyme de l'entité : Inria
Type d'entité : Recherche
Financement ERC de l'entité : 1500000.0 €

Mathematical optimization is the key to solving many problems in science, based on the observation that physical systems obey a general principle of least action. While some problems can be solved analytically, many more can only be solved via numerical algorithms. Research in this domain has proved essential over many years. In addition, science in general is changing. Increasingly, in biology, medicine, astronomy, chemistry, physics, large amounts of data are collected by constantly improving signal and image acquisition devices, that must be analyzed by sophisticated optimization tools. In this proposal, we consider handling optimization problems with large datasets. This means minimizing a cost function with a complex structure and many variables. The computational load for solving these problems is too great for even state-of-the-art algorithms. Thus, only relatively rudimentary data processing techniques are employed, reducing the quality of the results and limiting the outcomes that can be achieved via these novel instruments. New algorithms must be designed with computational scalability, robustness and versatility in mind. In this context, Majorization-Minimization (MM) approaches have a crucial role to play. They consist of a class of efficient and effective optimization algorithms that benefit from solid theoretical foundations. The MAJORIS project aims at proposing a breakthrough in MM algorithms, so that they remain efficient when dealing with big data. I propose to tackle several challenging questions concerning algorithm design. These include acceleration strategies, convergence analysis with complex costs and inexact schemes. I will also tackle practical, massively parallel and distributed architecture implementations. Three specific applications are targeted: super-resolution in multiphoton microscopy in biology; on-the-fly reconstruction for 3D breast tomosynthesis in medical imaging; and mass spectrometry data processing in chemistry.

Mots-clés : inverse problems,majorization minimization methods,image processing,large scale optimization,parallel computing,tomographic reconstruction,image deblurring,sparse signal restoration,convergence analysis,non convex optimization
Projet ERC : STRUCTURALISM (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Cultures and cultural production
Entité participante : University of Vienna — Pays : Autriche — Rôle : PI
Acronyme de l'entité : Univie
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1364097.0 €

Mathematical structuralism is a central position in contemporary philosophy of mathematics. It is the view that mathematical theories describe only abstract structures or structural properties of their subject fields. This project will investigate the mathematical and philosophical roots of structuralism in a ground-breaking way. The focus will be set on two historical developments in nineteenth century mathematics and early twentieth century philosophy of science: the first one concerns several conceptual changes in geometry between 1860 and 1910 that eventually led to a “structural turn” in the field. This includes the gradual implementation of model-theoretic techniques in geometrical reasoning, the study of geometrical theories by group-theoretic methods as well as the successive consolidation of formal axiomatics. The second development analyzed here concerns the beginnings of the philosophical reflection on structural mathematics between 1900 and 1940. This includes different attempts by thinkers such as Rudolf Carnap, Edmund Husserl, and Ernst Cassirer to spell out the philosophical implications of the new structuralist methodologies at work in modern geometry. The principal objective of the project is to provide the first comparative investigation of these early contributions to structuralism and their immediate mathematical background. The integrative study of the history and the philosophy of modern mathematics will not only change our conception of the evolution of the philosophy of mathematics in the twentieth century. It will also transform the ways in which we presently think about mathematics. Specifically, the project will provide new systematic insights relevant to contemporary structuralism, in particular a new understanding of notions such as mathematical structure, structure abstraction, and structural property, as well as of their significance for the philosophy of mathematical practice.

Mots-clés : history and philosophy of mathematics,mathematical structuralism