Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : ETHNICGOODS (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : CERCA Institution — Pays : Espagne — Rôle : PI
Acronyme de l'entité : CERCA;I-CERCA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1985088.0 €

Around the world, nationalist politicians see migrants and ethnic minorities as undeserved receivers of public goods. What underpins these exclusionary claims is the thesis that ethnic diversity impedes the prospects of economic progress and social welfare. Does this thesis withstand systematic inquiry? At a first glance, there are plenty of studies that link ethnic heterogeneity to the underprovision of public goods. An influential literature in political economy asserts a strong association between high levels of diversity and low levels of public service provision such as schooling or health care. States, Nationalism, and the Relationship between Ethnic Diversity and Public Goods Provision [ETHNICGOODS] aims to revisit and challenge this conventional wisdom, and the doggedly ahistorical perspective it implies. Instead of treating ethnic diversity as exogenous, the project explores the role of historical patterns of nation-building and state institutional development. I expect different nation-building modes—that is, whether states seek to assimilate, accommodate, or exclude minorities—to have distinct consequences for contemporary levels of diversity and collective goods provision. I further expect historical variations in the capacity of states to provide public goods to affect contemporary levels of heterogeneity and public goods provision. To develop and test this theoretical argument on a global scale, ETHNICGOODS will create two new, publicly available datasets and combine comparative-historical case studies and statistical analysis. With this ambitious focus the project aims to make a major contribution to our understanding of ethnic diversity, injecting a historical perspective into debates around the political and developmental consequences of heterogeneity. Finally, by connecting with a range of academic and non-academic audiences, ETHNICGOODS will influence public debates about ethnic diversity and its effects.

Mots-clés : comparative politics,political sociology,comparative historical sociology,race and ethnicity,nationalism,state formation,mixed methods,public goods provision
Projet ERC : MUSAiC (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Cultures and cultural production
Entité participante : University of Limerick — Pays : Irlande — Rôle : Partner
Acronyme de l'entité : UL
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 10000.0 €

Artificial intelligence (AI) is an especially disruptive technology, impacting a growing number of domains in ways both beneficial and detrimental. It is even showing surprising impacts in the Arts, provoking questions fundamental to philosophy, law, and engineering, not to mention practices in the Arts themselves. MUSAiC is an interdisciplinary research venture confronting questions and challenges at the frontier of the AI disruption of music. It aims to analyze, criticize and fundamentally broaden the AI transformation of three interrelated music practices: 1) listening, 2) composition and performance, and 3) analysis and criticism. For each practice, and grounded in two specific music traditions (Irish and Swedish), MUSAiC will document and critically analyze the impacts of and ethical issues surrounding AI. MUSAiC will formulate and implement the first music pedagogy for AI, the lack of which continues to result in the creation of AI systems that have only a surface knowledge of music. From this pedagogy, MUSAiC will develop new holistic methods for understanding and benchmarking AI, and improving them and their application. It will implement and test novel AI systems that dynamically adapt to specific users as “digital apprentices”, thus bringing human-AI music partnerships to new levels of fruitfulness. The outcomes of MUSAiC will facilitate applications of AI to music in robust and responsible ways, impacting a wide variety of stakeholders. It will not only prepare music practitioners and audiences of the present (human and artificial) for new ways of listening, working, appraising, and developing the art form, but will also pave the way for analyzing, criticizing and broadening the AI transformation of the other Arts. The PI, a leading figure in music AI and music informatics, is employed at a world-leading research department at the top-rated technical university in Sweden. He is also a composer, frequently illustrating his research outcomes through music.

Mots-clés : music pedagogy for ai
Projet ERC : GHOSTWORK (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Social World and Its Diversity
Entité participante : Erasmus University Rotterdam — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : EUR
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1958490.0 €

Artificial intelligence depends on human labour to conduct tasks such as data cleaning, coding, and classifying content. This on-demand work is offered and performed online, paid by the task, on platforms like Amazon Mechanical Turk. Conceptualized as ‘ghost work’, this rapidly growing, platform-based work is largely unseen: workers are unable to speak with managers, do not get feedback, and lack labour protections. How do these specific work conditions influence ghost workers’ well-being? To ensure decent work conditions as automation continues to expand, knowledge about the effects of ghost work on well-being is urgently needed. The proposed project will develop and test an integrative framework for analysing the effects of ghost work on worker’s well-being. Existing models for analysing the impact of work conditions on well-being fall short for studying ghost work, as these models assume a person has a job and most likely an employer and colleagues. Therefore, this project begins from the specificities of ghost work to synthesize theories and concepts about algorithmic control, occupational well-being, human computation, and platform labour, in order to understand how and through which mechanisms ghost work influences well-being. The project will contribute to and advance cross-disciplinary scholarship on platform labour and organizational studies of algorithmic technologies. Using a multi-methodological approach to study the effects of ghost work, it begins with in-depth interview-based fieldwork on ghostworkers’ work conditions, and then entails qualitative diary studies of the short-term dynamics of ghost work for worker’s work conditions and well-being. Finally, a 4-wave longitudinal panel study will investigate the relationship between ghost work and well-being over time. Scholars in multiple fields, as well as policy makers and industry leaders, will be keenly interested in both the resulting integrative framework and empirical findings.

Mots-clés : platform work,platform labour,digital labour,workers  well being,work conditions,algorithmic control,job control,algorithmic fairness,employee well being
Projet ERC : HEINE (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
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 : 1498445.0 €

Artificial nitrogen reduction to ammonia using the Haber-Bosch process directly supports half of the global food production and accounts for 2% of the global energy consumption. This large consumption of energy originates mostly from the use of H2 (derived from fossil fuels) as a reductant and from the high pressure and temperature required to undertake the Haber-Bosch process. Electrochemical synthesis of ammonia, using a proton and electron source combined with an electrocatalyst at room temperature to reduce N2, thus presents an appealing, energy-efficient alternative. However, despite years of research, the few currently available catalysts have very limited efficiency in N2 electroreduction. Drawing inspiration from biochemistry and using the tools of coordination chemistry, catalysis and surface chemistry, this project will explore an original strategy to develop catalysts for the reduction of N2 inspired by the nitrogenase enzyme. Motivated by the recent discovery of two unique moieties in the nitrogenase cofactor – the presence of a µ6-carbide moiety and a Mo(III) center – and of the increased understanding of substrate pathways in the nitrogenase protein structure, the goal of HEINE is to design new hybrid catalysts based on the immobilisation of accurate mimics of the nitrogenase active sites onto heterogeneous supports used to generate properties analogous of the protein scaffold (hydrophobicity, proton relays, etc.). This will provide us with novel ways to develop functional electrocatalysts for N2 reduction in ambient conditions, combining the activity of traditional solid-state systems, with the selectivity of molecular catalysts. By identifying and reproducing the parameters responsible for the unique activity of nitrogenase enzymes, HEINE will yield invaluable information on nature’s routes to N2 reduction and will pave the way towards a new generation of electrocatalysts able to promote this reaction.

Mots-clés : n2 reduction,cluster synthesis,low valent molybdenum,electrocatalysis,surface chemistry
Projet ERC : REFOCUS (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
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 : 1487597.0 €

As a Nobel-honored technology, optical frequency combs, which consist of equidistant spectral lines, have revolutionized applications in time-keeping, and metrology as they offer unprecedented precision in frequency via self-referencing. However, conventional frequency comb systems have been confined to laboratories due to the cost, size, and power requirements of their components. This project aims to develop a chip-scale optical frequency comb source that can be self-referenced. Key components to realize self-referencing are comb generators and frequency doublers. However, it is challenging to integrate both functionalities on the same chip as they typically rely on different nonlinear processes and thus different material platforms. Another major challenge in the system miniaturization is how to achieve ultra-efficient comb generation and frequency doubling to enable on-chip comb pumping and self-referencing beat note detection, respectively. In this project, we will circumvent the multi-material issue by developing both comb generator and frequency doubler based on the same nonlinear material: aluminum gallium arsenide (AlGaAs). This material exhibits both strong cubic and quadratic nonlinearities which can be utilized for comb generation and frequency doubling, respectively. Ultra-efficient comb generation will be realized by developing ultra-high-quality-factor microresonators and employing new comb generation methods combining cubic and quadratic nonlinearities while highly-efficient frequency doubling will be achieved by adaptively-controlling the phase-matching condition. We will also develop heterogeneous integration technologies to bridge the nonlinear devices with on-chip laser sources and detectors by using intermediate silicon nitride circuits. Successful miniaturization of a self-referenced frequency comb source will enable applications like LIDAR, coherent communications, chemical sensing, medical imaging, and precision metrology.

Mots-clés : integrated optics,nonlinear optics,optical frequency comb,nanofabrication
Projet ERC : bECOMiNG (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : University of Milan — Pays : Italie — Rôle : PI
Acronyme de l'entité : UNIMI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1998781.0 €

As an onco-hematologist with a strong expertise in genomics, I significantly contributed to the understanding of multiple myeloma (MM) heterogeneity and its evolution over time, driven by genotypic and phenotypic features carried by different subpopulations of cells. MM is preceded by prevalent, asymptomatic stages that may evolve with variable frequency, not accurately captured by current clinical prognostic scores. Supported by preliminary data, my hypothesis is that the same heterogeneity is present early on the disease course, and identification of the biological determinants of evolution at this stage will allow better prediction of its evolutionary trajectory, if not its control. In this proposal I will therefore make a sharp change from conventional approaches and move to early stages of MM using unique retrospective sample cohorts and ambitious prospective sampling. To identify clonal MM cells in the elderly before a monoclonal gammopathy can be detected, I will collect bone marrow (BM) from hundreds of hip replacement specimens, and analyze archive peripheral blood samples of thousands of healthy individuals with years of annotated clinical follow-up. This will identify early genomic alterations that are permissive to disease initiation/evolution and may serve as biomarkers for clinical screening. Through innovative, integrated single-cell genotyping and phenotyping of hundreds of asymptomatic MMs, I will functionally dissect heterogeneity and characterize the BM microenvironment to look for determinants of disease progression. Correlation with clinical outcome and mini-invasive serial sampling of circulating cell-free DNA will identify candidate biological markers to better predict evolution. Last, aggressive modelling of candidate early lesions and modifier screens will offer a list of vulnerabilities that could be exploited for rationale therapies. These methodologies will deliver a paradigm for the use of molecularly-driven precision medicine in cancer.

Mots-clés : cancer evolution,cancer heterogeneity,multiple myeloma,biomarkers
Projet ERC : EPOCHAL (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : Swiss Tropical and Public Health Institute — Pays : Suisse — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 1381931.5 €

As climate change increases the duration and intensity of the pollen season, allergies to airborne pollen are increasingly common in Europe. Yet, it is not well recognized that high pollen concentrations may increase respiratory and cardiovascular events, leading to mortality and excess hospitalizations. I aim to investigate how short-term exposure to pollen is related to mortality, hospitalization and allergic symptoms, both on its own and synergistically with air pollution and weather. I will develop spatiotemporal exposure models of pollen for the years 2003-2022 based on a network of 14 pollen measurements stations in Switzerland. Taking advantage of large, real-world datasets without selection bias (Swiss National Cohort) and the efficient case-crossover study design, I will investigate the population effects of pollen on daily respiratory and cardiovascular mortality and hospitalization, also accounting for variation in air pollution and weather conditions. To explore individual sensitivity, I will conduct repeated measurements of lung function and airway inflammation in a dedicated panel of 400 allergic patients complemented with opportunistic repeated accounts of self-reported symptoms from the “e-symptoms” app by Swiss Allergy Centre. To provide personalized prevention recommendations and enhance quality of life for the allergic population, I will derive exposure-response relationships based on prevalent pollen, air pollution and weather triggers and individual symptom reports, allowing me to ultimately forecast symptom severity using machine learning techniques. This highly innovative project utilizes available nationwide health datasets and systematic novel data collection methods (in the in-depth panel study), to better understand the role of pollen in respiratory and cardiovascular diseases at both personalized and population levels. The project will prevent and reduce health effects due to pollen, which constitute a large burden on health and economy.

Mots-clés : pollen,spatiotemporal modelling,respiratory disease,cardiovascular disease,mortality,allergic symptoms
Projet ERC : AGRICON (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Life Science
Panel : Environmental Biology, Ecology and Evolution
Entité participante : The Francis Crick Institute — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 1500000.0 €

As global climates warmed ca. 10,000 years ago came a remarkable convergent transformation of human lifestyles that occurred independently in multiple continents and human populations. This transition from hunter-gatherer subsistence to food-production catalysed large-scale population growth, offering the opportunity for increased rates of adaptation, but also rapidly presented a large number of independent human populations with a new evolutionary challenge. This project will use ancient population genomics—the only way to directly reconstruct human genetic evolution—to study whether evolutionary processes during the agricultural transition differed in differed regions. Which genomic adaptations were associated with the agricultural transition? Did adaptation to hunter-gatherer and agricultural lifestyles act on similar genetic architecture in different instances? To which extent did adaptation in domestic dogs—the only species domesticated prior to the agricultural transition—occur in convergence with humans? To answer these questions, the project will generate ancient genomic data from pre-agricultural and early agricultural populations from multiple human- and domestic dog populations from Africa, Central America, and Southeast Asia. This will be achieved with direct sequencing as well as a new human ~850,000 SNP capture panel designed to avoid bias towards Eurasian ancestry. We will also develop new computational methods robust to the challenges posed by ancient genomes to identify adaptive admixture, analyse copy number variation, test continuous population models, and statistically assess convergence in the genomic architecture of adaptation. Leveraging cutting-edge ancient genomics and two model organisms for the genomic basis of phenotypic variation, this project aims to reconstruct the universal evolutionary phenomena underpinning a watershed evolutionary episode that shapes global biodiversity and the human condition to this day.

Mots-clés : ancient dna,population genetics,evolution,archaeology,human origins,genomics,human,evolutionary genetics,neolithic
Projet ERC : COMPECON (Horizon 2020, appel 2016, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Hebrew University of Jerusalem — Pays : Israël — Rôle : PI
Acronyme de l'entité : HUJI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2026706.0 €

As more and more economic activity is moving to the Internet, familiar economic mechanisms are being deployed at unprecedented scales of size, speed, and complexity. In many cases this new complexity becomes the defining feature of the deployed economic mechanism and the quantitative difference becomes a key qualitative one. A well-studied example of such situations is how the humble single-item auction suddenly becomes a billion-times repeated online ad auction, or even becomes a combinatorial auction with exponentially many possible outcomes. Similar complexity explosions occur with various markets, with information dissemination, with pricing structures, and with many other economic mechanisms. The aim of this proposal is to study the role and implications of such complexity and to start developing a coherent economic theory that can handle it. We aim to identify various measures of complexity that are crucial bottlenecks and study them. Examples of such complexities include the amount of access to data, the length of the description of a mechanism, its communication requirements, the cognitive complexity required from users, and, of course, the associated computational complexity. On one hand we will attempt finding ways of effectively dealing with complexity when it is needed, and on the other hand, attempt avoiding complexity, when possible, replacing it with ``simple'' alternatives without incurring too large of a loss.

Mots-clés : games,auctions,fairness,concrete complexity,equilibrium,internet
Projet ERC : PIVOTAL (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Complexity
Entité participante : Goethe University Frankfurt — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1330126.5 €

As neuroscientific findings on brain functions accumulate, it is increasingly important to derive a set of overarching general principles about how the human brain works. For this, the predictive coding framework emerges as a promising route, with the notion that the brain operates as a prediction machine; internal models in the brain predict future states against which incoming information of new experience is compared. This new conceptual framework leads to two essential empirical questions that PIVOTAL will tackle: (1) What is the nature of the internal models on which predictions are generated and how do our actual experiences shape them? (2) How do prediction processes play out in human brains that are inherently diverse due to changes such as those caused by maturation and senescence. Addressing these issues is important for advancing our basic understanding of the neurocognitive architectures that enable the brain to perform adaptively in our environment, with predictive processing as a fundamental operation. PIVOTAL will integrate three separate strands of cognitive neuroscience research on predictive coding, memory systems, and lifespan development. By using functional magnetic resonance imaging (fMRI) in experimental research designs, we aim to unravel the cognitive and neural mechanisms that underlie predictive processing based on individuals’ memory of prior experience (episodic memory) and well-learned knowledge about the world (semantic memory). These mechanisms will be systematically examined in samples of children, younger adults, and older adults, who differ from each other in important ways due to divergence in developmental orientation (progression vs. conservation) and neurocognitive landscape (structural and functional integrity of memory neural circuits). By explicating a more dynamic version of the predictive brain principle, we can start addressing issues related to the emergence of disorders at particular time windows in life.

Mots-clés : predictive coding,memory systems,human life span development,cognitive neuroscience