Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : InfAct (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed matter physics
Entité participante : Tel Aviv University — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999125.0 €

The relation between information and thermodynamics remains a fundamental thought-provoking issue since the days of Maxwell. The most effective platforms used to explore this relation are information machines: processes that convert measured information about a system to extractable work. A crucial question that goes beyond the currently explored regime in the field is how information machines perform out of equilibrium, namely when not in equilibrium with a thermal bath. This is the general scenario of relevance to biological systems and stands at the focus of this proposal. Our recent result, showing how temporal correlations affect the efficiency of information engines (PRL 2018), demonstrates our ability to manipulate, track, and analyze particle motion in order to address such outstanding questions experimentally. We propose to realize and study three new information engines with increasing complexity: (1) a microscopic engine that converts information to osmotic pressure, (2) a macroscopic engine that converts information to pressure, and (3) a mobile engine converting local information to directed motion. The proposed engines are unique in that they harvest work from active systems, not necessarily coupled to a heat bath, and include many particles. By measuring work and information directly, we will be able to test the validity of the generalized second law of thermodynamics and the Jarzynski fluctuation theorem in active matter systems with feedback and control loops. Providing essential, precise and detailed experimental observations in a field in which they are lacking will pave the way toward the extension of stochastic thermodynamics to active systems. Unraveling the mechanisms that govern conversion of information to useful work will benefit far-reaching applications. These include macroscopic and microscopic biomimetic robots and machines made of an ensemble of simple agents, analogous to natural phenomena such as cargo transport in ant colonies

Mots-clés : statistical mechanics and thermodynamics far from thermal equilibrium,thermodynamics of information,active matter,information engines
Projet ERC : SmartCore (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : Graz University of Technology — Pays : Autriche — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1112000.0 €

The replication of the circle of information coming from the environment, to the skin, to an action mediated by the brain, requires a lot of advances in smart technology and materials development. Embedding sensors in smart architectures that record the stimulus from the environment and transform it into action is the objective of artificial skins. At the moment, different sensors have to be implemented in the artificial skin matrix for each stimulus. The goal of this project is to develop a single multi-stimuli responsive material, which would allow a simplification of the artificial skin and enable unprecedented spatial resolution. The material will be comprised of a smart core, responsive to temperature and humidity, and a piezoelectric shell for pressure sensing. The swelling of the smart core upon stimuli will be sensed by the piezoelectric shell and produce a measurable potential. This architecture will be achieved thanks to the use of novel vapor-based technologies for material processing that allow fabrication at the nanoscale. The advantage of using a dry, vapor-based, polymerization for the smart core is that it will be possible to cumulate different functionalities and engineered composition gradients, which are difficult to obtain by conventional synthesis. Nano-structuration of such materials in core-shell site-specific arrays will allow to create a sensing network with spatial resolution down to 1mm and lower. The network will respond to the stimuli coming from the environment and recognize them in terms of location and type of stimuli. The successful execution of the SmartCore project will have a strong impact in the design and production of future structures, with consequences in sensoring, biotechnology and tissue engineering.

Mots-clés : chemical vapor deposition,piezoelectricity,stimuli responsive materials
Projet ERC : DENOVO-RBC (Horizon 2020, appel 2014, Proof of concept grants)
Entité participante : University of Iceland — Pays : Islande — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

The research conducted in SYSTEM_US (ERC Advanced Grant #232816) has led to the discovery of valuable biomarkers for the quality of red blood cells (RBCs) in standard blood bank storage conditions. Using a combination of systems biology and metabolomics, this discovery will lead to the development of novel RBC storage technologies including additive solutions and diagnostics. These technologies will become valuable assets in the form of intellectual property to large transfusion medicine companies and will save thousands of lives over the coming decades. This proposal covers the needs to fully define the market and NPV of the developing technology, fully define the regulatory and clinical path of product approval, file for patents to protect the intellectual property, conduct necessary validation experiments, and continue to develop relations with industry contacts.

Mots-clés : transfusion,transfusion medicine,blood banking,blood,red blood cells,blood storage,additive solutions,diagnostics,metabolism,metabolomics,systems biology
Projet ERC : INFL (Horizon 2020, appel 2015, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : University of London — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : U. London
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1568276.0 €

The research in this proposal will contribute to our knowledge of the dynamics and effects of inflation through the perspective of risk-adjusted inflation and valuation formulas. The goal is to provide new perspectives on inflation and its impact on resource constraints. The first step in this work is to collect new data on inflation options and use them to construct marginal densities for risk-neutral inflation at different horizons. Because there are data for both cumulative and annual inflation, I propose a new non-parametric estimator of the joint distribution of inflation. This provides a new take on inflation dynamics and the anchoring of inflation expectations. The second step is to use these densities as discount factors to price nominal claims. Applying these to the assets and liabilities of a central bank gives a measure of the fiscal capacity of the central bank and the impact of a higher inflation target. The third step combines the densities for inflation with nominal interest rates to gauge how much society dislikes inflation. The risk-neutral densities give inflation strips that reveal how much markets would be willing to pay to eliminate inflation risk. The fourth step is to design inflation-indexed bonds that allow countries to self-insure in a currency union. Understanding how the value and payments of these bonds changes with different measure of inflation, a country can insure itself against some of the shocks that affect its real exchange rate. The fifth step is to design new liabilities of the central bank that allow for a better control of inflation. By issuing inflation-indexed reverses and maintaining the commitment to redeem them one-to-one for currency at all date, the central bank can control inflation. All together, these different projects aim to deepen our understanding of inflation, by focusing on modeling risk-adjusted inflation, to answer old questions and suggest new approaches in monetary economics.
Projet ERC : FLUID-INTERFACE (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : Universidad de Sevilla — Pays : Espagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1106936.0 €

The research of this proposal is focused on solving problems that involve the evolution of fluid interfaces. The project will investigate the dynamics of free boundaries arising between incompressible fluids of different nature. The main concern is well-posed scenarios which include the possible formation of singularities in finite time or existence of solutions for all time. These contour dynamics issues are governed by fundamental fluid mechanics equations such as the Euler, Navier-Stokes, Darcy and quasi-geostrophic systems. They model important problems such as water waves, viscous waves, Muskat, interface Hele-Shaw and SQG sharp front evolution. All these contour dynamics frameworks will be studied with emphasis on singularity formation and global existence results, not only for their importance in mathematical physics, but also for their mathematical interest. This presents huge challenges which will in particular require the use of different tools and methods from several areas of mathematics. A new technique, introduced to the field by the Principal Investigator, has already enabled the analysis of several singularity formations for the water waves and Muskat problems, as well as to obtain global existence results for Muskat. The main goal of this proposal is to develop upon this work, going far beyond the state of the art in these contour dynamics problems for incompressible fluids.

Mots-clés : incompresible flow,contour dynamics,euler,navier stokes,porous media,muskat,blow up,singularity
Projet ERC : REVENANT (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Cultures and cultural production
Entité participante : University of Rijeka — Pays : Croatie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1936185.0 €

The research project REVENANT will examine contemporacollective memories and legacies of the Habsburg, Ottoman, and Romanov Empires from a capacious, comparative perspective. Across the broad geography of central and eastern Europe, the Balkans, central Asia and the Caucasus, and the Middle East, bygone imperial projects are increasingly inseparable from contemporary political and cultural life. REVANANT will constitute the first comprehensive research project to grapple with these complex, overlapping post-imperial memories and legacies. Our overarching research question is: How do collective memories of the Habsburg, Ottoman and Romanov Empires achieve articulation through ensembles of persons, places and things? The threefold heuristic of persons, places, and things determines three secondary research questions. First, how and why do certain historical personages galvanize collective memories of empires? Secondly, how do specific places become sites of post-imperial memory? Thirdly, how are bygone empires embodied through a variety of objects and material culture? The research for REVENANT will be resolutely interdisciplinary, drawing on insights from Anthropology, Sociology, History, Comparative Literature, Art and Architectural History, Memory Studies, and Material Culture Studies. Accordingly, it will rely on a variety of methods, including participant observation, visual ethnography, archival research, targeted interviews with key actors, and discourse analysis. In addition to the principal investigator, the research group will consist of three postdoctoral fellows and three doctoral students. REVENANT will be based at the Cultural Studies Department of the University of Rijeka, Croatia; Rijeka will also be one of the project's research sites. The envisioned results and outputs of REVENANT include a variety of scholarly publications and academic symposia, as well as a documentary film, a photographic exhibition, and a multi-faceted project website.

Mots-clés : empire,post imperialism,collective memory,space and place,material culture,habsburg,ottoman,romanov
Projet ERC : ROMB (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : Ludwig-Maximilians-Universität München — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : LMU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1497175.0 €

The retina carries signatures of neuronal diseases which have been linked to mechanical abnormalities, including Glaucoma and Alzheimer’s disease. Yet no biophysical retina model exists due to a cross-disciplinary challenge: While stem cell derived organoids mimic the retina in vitro, organoid research has been limited by large variations in cell and tissue organization. Mechanobiology, in turn, has revealed mechanical signals as essential players in regulating cellular behavior to guide organogenesis. Accordingly, the ball is in the court of physicists and bioengineers to quantify those mechanical signals and shape tissue growth by tailoring the physical interactions of cells with their environment. Finally, the functionality of the retina has to be quantified via neuroscience techniques. This multifaceted challenge has prevented the establishment of the retina organoid as a biophysical model. ROMB introduces a biophysical model for the retina which I will use to model Alzheimer’s disease in vitro. It will be built on 4 cross-disciplinary posts: (i) retina organoids as a physiological in vitro model, (ii) tissue mechanics measurements, (iii) neuronal activity readout and (iv) disease modeling. First, I will reveal the mechanical building plan of retina organoids using ferrofluid droplets as mechanical actuators, hereby opening the field of organoid mechanobiology. In a second step, the organoid’s 3D neuronal function will be recorded using lightsheet microscopy. Mechanical, functional and genetic access will allow me in a final step to detect and manipulate Alzheimer’s disease: using mouse retina organoids with a mutation in the App gene, I will mechanically characterize the formation of those peptide aggregates which are the hallmark of disease onset. ROMB opens the door to engineering functional retinas in vitro. Moreover, it will be uniquely suited to tackle mechanically related neuronal diseases and promises a breakthrough for basic and applied research.

Mots-clés : tissue mechanics,neuronal network,retina organoid,alzheimer s disease
Projet ERC : DANCING (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : National University of Ireland — Pays : Irlande — Rôle : PI
Acronyme de l'entité : NUI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999337.0 €

The right of people with disabilities to participate in cultural life - which encompasses the rights of access to, and to be involved in cultural activities, as well as the recognition of disability identities, such as Deaf culture – has been for long denied. The cultural exclusion of disabled people has engendered their marginalisation. It has also entailed a loss for society as a whole, because of the lack of cultural diversity resulting from an inaccessible and exclusionary cultural realm. DANCING will investigate the extent to which the protection of the right to take part in culture of people with disabilities and the promotion of cultural diversity intersect and complement each other in the European Union (EU) legal order. It will disrupt the conventional approach adopted by EU law scholarship by using a combination of legal, empirical and arts-based research to pursue three complementary objectives, experiential, normative and theoretical respectively. First, it will identify and categorise barriers and facilitators to cultural participation experienced by disabled people and how they affect the wider cultural domain. Secondly, it will provide a normative exploration of how the EU has used and can use its competence to combat discrimination and its supporting competence on cultural matters, in synergy with its wide internal market powers, to ensure the accessibility of cultural activities, to promote disability identities, while achieving cultural diversity. In doing so, it will bridge, in an unprecedented way, the implementation of the UN Convention on the Rights of Persons with Disabilities and the UNESCO Convention on the Protection and Promotion of the Diversity of Cultural Expressions. Thirdly, it will advance the understanding of the legal concept of cultural diversity, which stems from the intersection of different sources of law, and will propose a new theorization of the promotion of cultural diversity within the EU legal order.

Mots-clés : disability rights,right to participate in cultural life,cultural diversity,access to culture,eu law,crpd,unesco convention on cultural diversity
Projet ERC : THUNDEEP (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Weizmann Institute of Science — Pays : Israël — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1442360.0 €

The rise of deep learning, in the form of artificial neural networks, has been the most dramatic and important development in machine learning over the past decade. Much more than a merely academic topic, deep learning is currently being widely adopted in industry, placed inside commercial products, and is expected to play a key role in anticipated technological leaps such as autonomous driving and general-purpose artificial intelligence. However, our scientific understanding of deep learning is woefully incomplete. Most methods to design and train these systems are based on rules-of-thumb and heuristics, and there is a drastic theory-practice gap in our understanding of why these systems work in practice. We believe this poses a significant risk to the long-term health of the field, as well as an obstacle to widening the applicability of deep learning beyond what has been achieved with current methods. Our goal is to tackle head-on this important problem, and develop principled tools for understanding, designing, and training deep learning systems, based on rigorous theoretical results. Our approach is to focus on three inter-related sources of performance losses in neural networks learning: Their optimization error (that is, how to train a given network in a computationally efficient manner); their estimation error (how to ensure that training a network on a finite training set will ensure good performance on future examples); and their approximation error (how architectural choices of the networks affect the type of functions they can compute). For each of these problems, we show how recent advances allow us to effectively approach them, and describe concrete preliminary results and ideas, which will serve as starting points and indicate the feasibility of this challenging project.

Mots-clés : learning theory,neural networks
Projet ERC : MICRORULES (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Life Science
Panel : Environmental Biology, Ecology and Evolution
Entité participante : Max Planck Society — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : MPG
Type d'entité : Recherche
Financement ERC de l'entité : 1497547.0 €

The roots of healthy plants are colonized by a rich diversity of microbes, forming multi-kingdom microbial consortia that impact plant productivity. Despite the expected high connectivity between root microbiota members, our understanding of microbe-microbe interactions in structuring microbial networks in plant roots as well as their functional impact on plant growth remains poorly understood. Based on recent findings in my group, I propose a conceptual framework aiming at a functional understanding the holo-plant microbiome, where microbial interactions play an integral role in structuring root-associated microbial communities and maintaining microbiota balance and plant health. By profiling three independently-evolved microbial classes (bacteria, fungi, oomycetes) in the roots of natural Arabidopsis thaliana populations across Europe and establishing corresponding reference culture collections for subsequent reconstitution of the plant microbiota in vitro, I will define fundamental mechanisms underlying the structure and functions of the plant microbiota. I will generate and utilize extensive microbial genome resources for the interpretation of metatranscriptome profiles of multi-kingdom synthetic communities during root colonization. This will contribute to a transition from binary plant-microbe to community-level molecular investigations. Finally, using a genetically tractable tripartite interaction model between the non mycorrhizal plant A. thaliana, a beneficial fungal root endophyte and a rhizobacterium, I propose to functionally dissect the molecular basis of beneficial multitrophic plant-microbe interactions by identifying microbial genes that are essential for both microbe-microbe and microbe-host interactions. My hypothesis-driven research applies innovative reductionist approaches to reconstitute the microbiota of plant roots in laboratory settings to identify fundamental assembly rules and mechanisms that underpin complex plant-microbe interactions.

Mots-clés : plant microbiome,microbial interactions,microbial genomics