Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : SOCMED (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The study of the human past
Entité participante : Humboldt-Universität zu Berlin — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : HU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Socialist Medicine: An Alternative Global Health History The project pioneers a new history of global health that, for the first time, incorporates the socialist world - a constellation of countries in a fluctuating political, economic and military nexus distinct from the capitalist West. It identifies the particular health cultures produced by socialism (in all its variety) and explores the impact of socialist internationalism in co-producing global health in the 20th century. The proposed project pioneers a new history that will not only transform our knowledge of historical processes, but will further our understanding of ideas, practices and processes that current global health structures have been built on. Global health histories are framed mainly through American, colonial and liberal perspectives, even as some contributions of the socialist world, e.g. in smallpox eradication, have been acknowledged. The omission of socialist contexts, however, distorts our understanding of what global health is. Many parts of the socialist world, like China or Czechoslovakia, provided different approaches to international and global health, e.g. in rural health or epidemic management. Although there was not one socialist template, diverse framings of socialist medicine played major roles in shaping and contesting global practices. A systematic analysis of socialist medicine and international health through global case studies integrates missing expert networks, political agendas, public health models and diplomatic agreements in global health history. This work, in turn, allows us to rethink concepts such as socialism, medical aid, solidarity, development, socialist medical research and health provision.

Mots-clés : global health history,cold war,socialist internationalism,history of medicine
Projet ERC : SOCMED (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The study of the human past
Entité participante : University of Exeter — Pays : Royaume-Uni — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 0.0 €

Socialist Medicine: An Alternative Global Health History The project pioneers a new history of global health that, for the first time, incorporates the socialist world - a constellation of countries in a fluctuating political, economic and military nexus distinct from the capitalist West. It identifies the particular health cultures produced by socialism (in all its variety) and explores the impact of socialist internationalism in co-producing global health in the 20th century. The proposed project pioneers a new history that will not only transform our knowledge of historical processes, but will further our understanding of ideas, practices and processes that current global health structures have been built on. Global health histories are framed mainly through American, colonial and liberal perspectives, even as some contributions of the socialist world, e.g. in smallpox eradication, have been acknowledged. The omission of socialist contexts, however, distorts our understanding of what global health is. Many parts of the socialist world, like China or Czechoslovakia, provided different approaches to international and global health, e.g. in rural health or epidemic management. Although there was not one socialist template, diverse framings of socialist medicine played major roles in shaping and contesting global practices. A systematic analysis of socialist medicine and international health through global case studies integrates missing expert networks, political agendas, public health models and diplomatic agreements in global health history. This work, in turn, allows us to rethink concepts such as socialism, medical aid, solidarity, development, socialist medical research and health provision.

Mots-clés : global health history,cold war,socialist internationalism,history of medicine
Projet ERC : NoTape (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Technical University of Denmark — Pays : Danemark — Rôle : PI
Acronyme de l'entité : DTU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1463805.0 €

Society generates increasing amounts of data, which is both a resource and a challenge. The data reveal new insights that may potentially improve our livelihood, but their quantity renders such insights difficult to find. Machine learning techniques sift through the data looking for statistical patterns of interest to a given task. Due to an exponential growth in available data, these techniques enable us to automate difficult decisions, such as those needed for personalized medicine and self-driving cars. NoTape note that machine learning techniques depend on a distance measure to determine which data points are similar and which are not. As this measure is difficult to choose, NoTape develop methods for estimating an optimal distance measure directly from data. Empirical evidence suggest that the optimal distance measure in one region of data space need not coincide with the optimal measure in another region, i.e.that the distance measure should locally adapt to the data. Local adaptability imply that the distance measure itself will be sensitive to noise in the data, and therefore should be described as a random variable. NoTape estimate distance measures as random Riemannian metrics and perform statistical data analysis accordingly. The notion of statistical computations with respect to an uncertain locally adaptive distance measure is uncharted territory, which need new algorithms for numerical integration and for solving differential equations. As a guiding example, we estimate statistical models that reflect human perception. As perception processes are not fully understood, an optimal distance measure cannot be precisely estimated and the uncertainty of NoTape is needed. The geometric nature of the developed methods ensure that attained models are interpretable by humans, which contrast current locally adaptive techniques. As society automate more decisions, interpretability is increasing important to ensure that the machine learning system can be trusted

Mots-clés : machine learning,differential geometry,riemannian geometry,metric learning,numerical analysis,statistics on manifolds
Projet ERC : RESEDA (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Social World and Its Diversity
Entité participante : Institut national de la santé et de la recherche médicale — Pays : France — Rôle : PI
Acronyme de l'entité : Inserm
Type d'entité : Recherche
Financement ERC de l'entité : 1541617.0 €

Socioeconomic inequalities in children’s neurodevelopment and mental health are observed from early onwards and widen over time. Moreover, children whose parents are immigrant, particularly if they belong to ethnic minority groups, may be especially vulnerable. Yet there are important inter-individual differences in development, implying the possibility of resilience. My project will examine the consequences of multiple forms of socioeconomic adversity in children’s family and broader social environment with regard to their neurodevelopment and mental health, testing the role of social supports as sources of resilience. Specifically, I will rely upon longitudinal data collected from the ELFE child cohort study, a nationally representative sample of 18 321 children born in France in 2011 and followed-up to age 10.5 years, which will be linked with longitudinal administrative and geographical information characterizing neighbourhoods of children’s school and residence, as well as healthcare use data. Potential resilience factors will include familial (e.g. relations between the child and his/her mother and father, grandparents’ involvement) and contextual social supports (e.g. childcare prior to school entry, neighborhood social capital). Lifecourse patterns of adversity and resilience at each level of analysis will be identified using statistical methods developed for high-dimensional data and their influence on children’s development will be ascertained applying methods that strengthen causal inference (e.g. propensity scores). The results will help clarify 1) the ways in which lifecourse patterns of exposure to adversity in the family and children’s broader social environment can influence neurodevelopment and mental health, particularly among children of immigrants; 2) familial and collective factors that can help children overcome the odds and should be promoted.

Mots-clés : migration,children,resilience,neurodevelopment,mental health
Projet ERC : MIMIC (Horizon 2020, appel 2015, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed matter physics
Entité participante : National Research Council — Pays : Italie — Rôle : PI
Acronyme de l'entité : CNR
Type d'entité : Recherche
Financement ERC de l'entité : 1314375.0 €

Soft matter provides the ideal playground for exploring physical phenomena that have no counterpart in atomic and molecular systems. A continuous progress in particle synthesis has provided a rich variety of soft, polymeric colloids, which are highly interpenetrable and can reach ultra-dense, jammed states. Such colloids offer exquisite control of material properties through a change in their internal architecture. Among this new generation of soft particles, microgels – colloidal-scale particles individually made by crosslinked polymer networks – have become a favourite model system for their responsive swelling properties and their multitude of applications. Notwithstanding their potentialities, knowledge of their behaviour from a fundamental point of view is still very limited. The present theoretical description is mostly based on simple models, which do not account for the internal, polymeric nature of the particles. Using state-of-the-art computational techniques across all scales (from atomistic to multi-blob coarse-graining), this 5-years work-program will provide an accurate model of both the microgels and of the effective interactions among them. The model will account for polymer/solvent interactions and for variation of the external control parameters at all densities, up to jamming conditions. In this way, I will develop a unified framework from the design at the molecular level of the individual particle up to the description of the macroscopic properties of the material. At all steps, I will verify my theoretical progress with experimental measurements performed by world-leading collaborators. This proposal will thus bring the current understanding of microgels to a new level: besides rationalizing existing results, it will open the way for new uses and applications of these fascinating systems.

Mots-clés : microgels,effective potentials,computational methods for soft matter,coarse graining,polymers,phase diagrams,viscoelastic properties
Projet ERC : DEVINTA (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Università della Svizzera italiana — Pays : Suisse — Rôle : PI
Acronyme de l'entité : USI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499093.0 €

Software development, also due to the complexity of software systems, is tackled by collaborating teams of people. For example, in pair programming two developers share a single workstation: One writes code, while the other reviews it and proposes ideas for improvement. While interacting with teammates is the preferred means to obtain help, their availability may fall short and they might not always have a solution at hand. To address this problem, recommender systems for developers have been proposed in the form of applications providing information items considered valuable for a given task and context (e.g., recommending documentation useful to understand a code component). However, these recommenders can hardly be considered as a valid alternative to teammates since they are not proactive, do not support the developer in a wide variety of tasks, do not adapt their recommendations to the developer’s profile, and fall short in having meaningful interactions with the developer (e.g., they are not able to answer developers’ questions). Given the advances in artificial intelligence, the vision that one day a developer's teammate is a machine does not seem far-fetched anymore. The DEVINTA project aims at making this future a reality, introducing models and techniques serving as the basis for the first artificial assistant supporting developers in their everyday activities. The assistant helps in (i) comprehending unfamiliar code via source code to natural language translation; (ii) writing code faster, providing predictive feature implementation, meaning the ability to infer the feature the developer is implementing and automatically complete it; and (iii) improve code quality, performing online code review while the developer types the code, timely catching bugs and suboptimal implementation choices and providing solutions for them. The assistant uses advanced conversational interfaces to interact with the developer, answer her questions, and disambiguate her needs.

Mots-clés : software engineering,recommender systems for software developers,software quality
Projet ERC : SAFE (Horizon 2020, appel 2020, Proof of concept grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : RWTH Aachen University — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : RWTH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Software faults that evade testing can have serious consequences, not sparing economic damages or human losses. Recent developments towards multi-core processors have led to the increased usage of concurrency to utilize the hardware's full potential. While helping to make software ever more capable, this further complicates the already laborious and time-consuming challenge of software testing. Not only tremendously increasing the computational effort needed to automatically find concurrency bugs, such errors prove to be especially tricky to find and fix even for professional software developers. Yet, currently the market does not provide a satisfying solution to addresses this challenge. The PoC project SAFE, however, offers a comprehensive, rigorous, and automated methodology for systematically finding errors in concurrent software. Based on state-of-the-art technology, an academic prototype developed as part of the ERC CoG SYMBIOSYS project has established the algorithmic applicability of the core methodology, already finding a number of bugs in real-world concurrent software. The project includes 5 tasks: (1) To establish the technical requirements for transitioning the prototype into a product, the required engineering tasks will be defined. (2) To complete core features and laying technical groundwork for commercialization, the technology will be engineered to serve a wider range of software. (3) To secure the exploitation strategy through a consolidated IP position, the legal framework for license agreements and IPR strategy will be laid. (4) To raise awareness for SAFE, the market niche to be targeted will be identified by completing a market analysis, including an account of the competitive landscape. (5) To validate commercial feasibility and create a business plan for exploitation beyond SAFE, various exploitation strategies will be evaluated, such as the foundation of a Softw.-as-a-Service spin-off; resulting in a business plan and a feasibility report.

Mots-clés : software analysis,safety,concurrency
Projet ERC : FRAGMENT (Horizon 2020, appel 2017, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : Barcelona Supercomputing Center — Pays : Espagne — Rôle : PI
Acronyme de l'entité : BSC
Type d'entité : Recherche
Financement ERC de l'entité : 1279037.5 €

Soil dust aerosols are mixtures of different minerals, whose relative abundances, particle size distribution (PSD), shape, surface topography and mixing state influence their effect upon climate. However, Earth System Models typically assume that dust aerosols have a globally uniform composition, neglecting the known regional variations in the mineralogy of the sources. The goal of FRAGMENT is to understand and constrain the global mineralogical composition of dust along with its effects upon climate. The representation of the global dust mineralogy is hindered by our limited knowledge of the global soil mineral content and our incomplete understanding of the emitted dust PSD in terms of its constituent minerals that results from the fragmentation of soil aggregates during wind erosion. The emitted PSD affects the duration of particle transport and thus each mineral’s global distribution, along with its specific effect upon climate. Coincident observations of the emitted dust and soil PSD are scarce and do not characterize the mineralogy. In addition, the existing theoretical paradigms disagree fundamentally on multiple aspects. We will contribute new fundamental understanding of the size-resolved mineralogy of dust at emission and its relationship with the parent soil, based on an unprecedented ensemble of measurement campaigns that have been designed to thoroughly test our theoretical hypotheses. To improve knowledge of the global soil mineral content, we will evaluate and use available remote hyperspectral imaging, which is unprecedented in the context of dust modelling. Our new methods will anticipate the coming innovation of retrieving soil mineralogy through high-quality spaceborne hyperspectral measurements. Finally, we will generate integrated and quantitative knowledge of the role of dust mineralogy in dust-radiation, dust-chemistry and dust-cloud interactions based on modeling experiments constrained with our theoretical innovations and field measurements.

Mots-clés : dust aerosols,mineralogy,physics of emission,climate,modelling,spectroscopy
Projet ERC : SOLENALGAE (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems Engineering
Entité participante : Politecnico di Milano — Pays : Italie — Rôle : Partner
Acronyme de l'entité : POLIMI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 100000.0 €

Solar Energy is the most abundant renewable energy source available for our Planet. Light energy conversion into chemical energy by photosynthetic organisms is indeed the main conversion energy step, which originated high energy containing fossil deposits, now being depleted. By the way, plant or algae biomass may still be used to produce biofuels, as bio-ethanol, bio-diesel and bio-hydrogen. Microalgae exploitation for biofuels production have the considerable advantages of being sustainable and not in competition with food production, since not-arable lands, waste water and industrial gasses can be used for algae cultivation. Considering that only 45% of the sunlight covers the range of wavelengths that can be absorbed and used for photosynthesis, the maximum photosynthetic efficiency achievable in microalgae is 10%. On these bases, a photobioreactor carrying 600 l/m-2 would produce 294 Tons/ha/year of biomass of which 30% to 80%, depending on strain and growth conditions, being oil. However this potential has not been exploited yet, since biomass and biofuels yield on industrial scale obtained up to now were relatively low and with high costs of production. The main limitation encountered for sustained biomass production in microalgae by sunlight conversion is low light use efficiency, reduced from the theoretical value of 10% to 1-3%. This low light use efficiency is mainly due to a combined effect of reduced light penetration to deeper layers in highly pigmented cultures, where light available is almost completely absorbed by the outer layers, and an extremely high (up to 80%) thermal dissipation of the light absorbed. This project aims to investigate the molecular basis for efficient light energy conversion into chemical energy, in order to significantly increase the biomass production in microalgae combining a solid investigation of the principles of light energy conversion with biotechnological engineering of algal strains.

Mots-clés : photosynthesis,solar energy conversion,photoprotection
Projet ERC : UQMSI (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
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 : 1733767.0 €

Some of the most important and exciting challenges of our ‘information age’ have led to the development of novel statistical methodology and algorithms that are designed to deal with inference settings involving high-dimensionality, graphical and network structures, inverse problems, ‘big data’, stochastic differential equations, diffusion processes, cosmic microwave background maps, brain tomography etc. While an abundance of algorithms is now available, a scientifically rigorous theory of uncertainty quantification and statistical decision making for such procedures has not been developed yet. Traditional approaches such as maximum likelihood estimation or parametric Bayesian inference cannot be used naively in increasingly complex contemporary statistical models. The construction of confidence statements and critical values for significant hypothesis tests is, however, of crucial importance for all applications of the statistical sciences to the modern world. In this research we propose an objective, mathematically rigorous, and practical paradigm for uncertainty quantification in modern statistical inference problems, and illustrate how this approach can be used in some of the recently emerged areas of statistics. Our theory can validate both Bayesian and frequentist approaches to statistical inference, and can be expected to be optimal in an information-theoretic sense. It has potential impact on all areas of scientific theory building, on legal and medical practice, public management of the internet, modern media and other information structures, and also on the foundations of the mathematical discipline of statistics in itself.