Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : DiaDev (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Cultures and cultural production
Entité participante : University of London — Pays : Royaume-Uni — Rôle : Partner
Acronyme de l'entité : U. London
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 123563.75 €

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 : DiaDev (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Cultures and cultural production
Entité participante : University of Edinburgh — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1306313.59 €

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 : MOLEQULE (Horizon 2020, appel 2015, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
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 : 1998638.0 €

Many physical and chemical processes in nature as well as an increasing number of man-made devices exploit the quantum properties of electrons, nuclei, and the quantum signatures of the coupling between nuclear and electronic motions. To optimize the design of novel devices and to correctly interpret physical processes studied, e.g., by experiments probing the molecular dynamics induced by interactions with ultrafast laser pulses, quantitative simulations are required. Although ninety years have passed since the discovery of Schrödinger’s equation, these simulations remain extremely difficult for systems with more than a few degrees of freedom. While some physicists are satisfied with a theoretical model that describes the system qualitatively, in chemistry the promising term ``ab initio quantum molecular dynamics'' is frequently misused for methods treating nuclear motion classically and using quantum mechanics only for electrons. The first goal of this project is, therefore, to bridge these two philosophies and combine accurate ab initio electronic structure calculations with accurate quantum or semiclassical treatment of the nuclear dynamics. Since the exact solution of time-dependent Schrödinger’s equation scales exponentially with the number of atoms, accelerating computers even by orders of magnitude will not break the exponential barrier to simulating molecular quantum dynamics. The second goal of this project is, therefore, developing and implementing both exact and approximate computationally efficient quantum dynamics methods applicable to polyatomic molecules. The last goal of the project is developing systematic methods for interpreting spectra of complex systems in terms of the underlying nuclear and electronic dynamics. To summarize in simple terms, the ultimate objective is developing theoretical methods that will allow replacing the popular classical molecular dynamics movies by their quantum analogs.
Projet ERC : SOTUF (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : Centre national de la recherche scientifique — Pays : France — Rôle : PI
Acronyme de l'entité : CNRS
Type d'entité : Recherche
Financement ERC de l'entité : 1436330.0 €

Many practical systems emit soot into the atmosphere as a result of incomplete combustion of hydrocarbons. This pollutant emission is characterized by a distribution of solid carbon particles with different sizes and shapes, which have negative effects on human health and environment. Controlling such emission represents a societal issue and an industrial challenge that require a deep understanding of the intricate processes underlying soot production in the turbulent flames that generally characterize practical systems. In this context, progress in numerical simulations is essential to the successful design of low-emission combustion systems. Unfortunately, the Large-Eddy Simultations (LES) approach, which has successfully demonstrated its capacity to represent gaseous turbulent combustion processes, is far from being predictive for soot emission. Indeed, soot production in turbulent flames is a complex process which is not easy to be represented with the classical LES strategy: the long time scales and the broad range of length scales place soot processes outside the usual scale ranges of LES subgrid models. In this context, the goal of the present project is to provide new insights on the processes governing soot production in turbulent flames to develop novel LES models, encompassing the state-of-art and allowing reliable predictions of soot in turbulent flames. These objectives will be achieved by: (1) characterizing the turbulence-flame-soot coupling from novel well-controlled experiments employing advanced space and time resolved optical diagnostics; (2) developing new subgrid models based on information extracted from experiments and high-fidelity simulations; (3) validating and applying the developed LES modeling strategy on complex systems. The research results are expected to drastically improve the prediction of soot production in industrial configurations, helping to design new low-emission systems with notably reduced soot levels.

Mots-clés : combustion,soot,modeling and numerical simulation,high speed diagnostics,turbulent flames
Projet ERC : HDXchip (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : University of Copenhagen — Pays : Danemark — Rôle : PI
Acronyme de l'entité : UCPH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999752.0 €

Many proteins important in biology or disease are not amenable to traditional methods for structural analysis as they cannot be isolated in sufficient amounts nor studied directly in their native environment. Alternative techniques are needed to understand the conformation and interactions of such difficult proteins and further support the on-going shift to protein-based ‘biopharmaceuticals’ in the pharmaceutical industry. One promising technique to meet this challenge is the use of mass spectrometry to measure the hydrogen/deuterium exchange of proteins (HDX-MS). However, HDX-MS suffers from significant limitations to realize its full potential and applicability. HDXchip aims to develop innovative microchips to revolutionize the HDX-MS method and enable analysis of difficult protein systems of unprecedented complexity. HDXchip will create “HDX-MS 2.0” technology that integrates on-chip sample treatment and separation to overcome major limitations of the conventional method, including virtually eliminating unwanted deuterium loss (back-exchange). The key to fabrication of HDXchips lie in the fact that all elements will be made of thiol-ene polymers. These polymers are not only used to make the chip itself in a cheap and simple manner, but also to fabricate high surface area functionalities for fast sample treatment (incl. enzymatic reactions) and separation at unprecedented efficiency. HDXchip will enable step-change improvements in popular HDX-MS applications, like analysis of the conformation and interactions of purified proteins and also open up for exciting and hitherto unfeasible applications such as analysis of proteins inside whole cells and proteins in their native lipid membranes. Hence, HDXchip could enable breakthrough insights into the conformation of difficult protein systems both in isolation and in their native environment, and dramatically improve our understanding of the function of such proteins and their potential to be targeted by drugs.

Mots-clés : hydrogen,deuterium exchange mass spectrometry,protein dynamics and interactions,microfluidics
Projet ERC : CAFES (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Amsterdam — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : UvA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1405652.0 €

Many questions in science, policy making and everyday life are of a causal nature: how would changing A influence B? Causal inference, a branch of statistics and machine learning, studies how cause-effect relationships can be discovered from data and how these can be used for making predictions in situations where a system has been perturbed by an external intervention. The ability to reliably make such causal predictions is of great value for practical applications in a variety of disciplines. Over the last two decades, remarkable progress has been made in the field. However, even though state-of-the-art causal inference algorithms work well on simulated data when all their assumptions are met, there is still a considerable gap between theory and practice. The goal of CAFES is to bridge that gap by developing theory and algorithms that will enable large-scale applications of causal inference in various challenging domains in science, industry and decision making. The key challenge that will be addressed is how to deal with cyclic causal relationships ("feedback loops"). Feedback loops are very common in many domains (e.g., biology, economy and climatology), but have mostly been ignored so far in the field. Building on recently established connections between dynamical systems and causal models, CAFES will develop theory and algorithms for causal modeling, reasoning, discovery and prediction for cyclic causal systems. Extensions to stationary and non-stationary processes will be developed to advance the state-of-the-art in causal analysis of time-series data. In order to optimally use available resources, computationally efficient and statistically robust algorithms for causal inference from observational and interventional data in the context of confounders and feedback will be developed. The work will be done with a strong focus on applications in molecular biology, one of the most promising areas for automated causal inference from data.

Mots-clés : causal discovery,causal inference,feedback,interventional data,time series data,molecular biology
Projet ERC : DYNAMICMARCH (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Cambridge — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : U. Cambridge
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1400789.0 €

Markov chains are fundamental processes and have been studied in nearly every scientific discipline in the past 100 years. In computer science, reversible Markov chains, also known as random walks, form the basis of many efficient randomised algorithms which have been successfully applied to a variety of complex sampling, learning and optimisation problems. Nowadays an increasing number of algorithms and processes on networks are based on multiple (concurrent and possibly dependent) random walks, including algorithms for packet routing, content search, graph clustering, link prediction and website ranking. This trend will be reinforced by the steady growth of large-scale networks and massive data sets. However, the existing theory of single random walks is not well-suited to cope with the complexity inherent to multiple random walks, and even for independent walks many fundamental questions remain open. The goal of this proposal is to develop a systematic and rigorous study of multiple random walks. First, we will analyse this random process via commonly used metrics such as hitting times, cover times, mixing times as well as new quantities which are unique to multiple random walks. Then we will connect these quantities to structural properties of the underlying graph. Finally, these insights will be applied to the design of new efficient randomised algorithms for large graphs and distributed networks.

Mots-clés : randomised algorithms,markov chains,distributed computing,random walks
Projet ERC : BinCosmos (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : University of Amsterdam — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : UvA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1926634.0 €

Massive stars play many key roles in Astrophysics. As COSMIC ENGINES they transformed the pristine Universe left after the Big Bang into our modern Universe. We use massive stars, their explosions and products as COSMIC PROBES to study the conditions in the distant Universe and the extreme physics inaccessible at earth. Models of massive stars are thus widely applied. A central common assumption is that massive stars are non-rotating single objects, in stark contrast with new data. Recent studies show that majority (70% according to our data) will experience severe interaction with a companion (Sana, de Mink et al. Science 2012). I propose to conduct the most ambitious and extensive exploration to date of the effects of binarity and rotation on the lives and fates of massive stars to (I) transform our understanding of the complex physical processes and how they operate in the vast parameter space and (II) explore the cosmological implications after calibrating and verifying the models. To achieve this ambitious objective I will use an innovative computational approach that combines the strength of two highly complementary codes and seek direct confrontation with observations to overcome the computational challenges that inhibited previous work. This timely project will provide the urgent theory framework needed for interpretation and guiding of observing programs with the new facilities (JWST, LSST, aLIGO/VIRGO). Public release of the model grids and code will ensure wide impact of this project. I am in the unique position to successfully lead this project because of my (i) extensive experience modeling the complex physical processes, (ii) leading role in introducing large statistical simulations in the massive star community and (iii) direct involvement in surveys that will be used in this project.

Mots-clés : massive stars,interacting binary systems,stellar populations,supernova progenitors,feedback,population synthesis
Projet ERC : MASTFAST (Horizon 2020, appel 2017, Proof of concept grants)
Entité participante : University of Edinburgh — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 148914.0 €

Mast cells are involved in the allergic response, anaphylaxsis, wound healing and angiogenesis. When activated via IgE, damage/pathogen-induced molecules or complement components, the granules of mast cells release proteases. Dysregulation of mast cells is implicated in allergies of the skin and airways, autism, chronic fatigue syndrome, pain, mastocytoma and intestinal dysmotility. Strategies to limit the damaging effects of mast calls are needed. However, the development of novel treatments is hampered by the inability to grow mast cells rapidly and efficiently in culture. Bone marrow cell cultures typically take 12 weeks before mast cells are available for study. Even then, there are too few cells to use for drug discovery or patient-specific treatment strategies. Embryonic stem cells (ESC) represent an alternative method for the production of mast cells. An unexpected ground-breaking discovery from our ERC AdG 341096 studies aiming to produce hematopoietic stem cells, was the development of a novel method that instead produces large numbers of mast cells in a short time. Mouse ESC engineered with a unique reporter gene that allows for cell enrichment during a multi-step culture yields a homogenous population of phenotypic and functional connective tissue and mucosal mast cells. Within only 3 weeks large numbers of mast cells are generated. To translate this method for large scale rapid production of human mast cells we will 1) characterize unique human reporter ESCs made by Crispr/CAS9 state-of-the-art method; 2) optimize human mast cell production from reporter hESCs in a multi-step differentiation culture; 3) characterize/validate the function of hESC-derived mast cells (normal and mutant); 4) interact with industry to use hESC-derived mast cells for drug-discovery and studies of mast cell differentiation and dysfunction. Long-term goals include the development of mast cell treatment strategies for personalized medicine.

Mots-clés : mast cells,differentiation,expansion,drug discovery,allergy,asthma,mastocytosis,inflammation,stem cells
Projet ERC : ADAM (Horizon 2020, appel 2019, Synergy grants)
Entité participante : University of Liverpool — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 4119096.0 €

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