Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : CARBOTIGHT (Horizon 2020, appel 2015, Proof of concept grants)
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 : 149500.0 €

Materials with excellent diffusion barrier properties are highly relevant for packaging applications (food, pharmaceutics), sealing (car tires), protective encapsulation (microelectronics, photovoltaics, displays), and anticorrosive coatings (automotive). In all these fields of application, there is a strong technological demand for more effective, less costly, and environmentally benign solutions, which constitutes a significant business opportunity. The proposed project aims to develop novel barrier layers and anticorrosive coatings based on functionalized carbon nanosheets that are prepared from reactive, carbon-rich molecular precursors with chemical functional groups that provide surface-specific binding and adhesion. These materials combine the excellent barrier and anticorrosive properties of atomically dense carbon or inorganic thin film coatings with the tailored surface properties of monolayer coatings. Moreover, their preparation will be compatible with scalable and inexpensive solution-phase processing methods such as painting, spraying, or printing, followed by UV curing. The goal of the proposed project is to provide technology demonstrators for a diffusion barrier layer aimed at packaging applications, as well as for a wear-resistant, anti-corrosive coating on a metal surface.

Mots-clés : diffusion barrier layers,anticorrosive coatings,carbon nanosheets,surface adhesion,oleophobic coating,nanocoating
Projet ERC : LocalOrder (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
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 : 1136904.0 €

Mathematical statistical physics has seen spectacular progress in recent years. Existing problems which were previously unattainable were solved, opening a way to approach some of the classical open questions in the field. The proposed research focuses on phenomena of localization and long-range order in physical systems of large size, identifying several fundamental questions lying at the interface of Statistical Physics, Probability Theory and Combinatorics. One circle of questions concerns the fluctuation behavior of random surfaces, where the PI has recently proved delocalization in two dimensions answering a 1975 question of Brascamp, Lieb and Lebowitz. A main goal of the research is to establish some of the long-standing universality conjectures for random surfaces. This study is also tied to the localization features of random operators, such as random Schrodinger operators and band matrices, as well as those of reinforced random walks. The PI intends to develop this connection further to bring the state-of-the-art to the conjectured thresholds. A second circle of questions regards long-range order in high-dimensional systems. This phenomenon is predicted to encompass many models of statistical physics but rigorous results are quite limited. A notable example is the PI’s proof of Kotecky’s 1985 conjecture on the rigidity of proper 3-colorings in high dimensions. The methods used in this context are not limited to high dimensions and were recently used by the PI to prove the analogue for the loop O(n) model of Polyakov’s 1975 prediction that the 2D Heisenberg model and its higher spin versions exhibit exponential decay of correlations at any temperature. Lastly, statistical physics methods are proposed for solving purely combinatorial problems. The PI has applied this approach successfully to solve questions of existence and asymptotics for combinatorial structures and intends to develop it further to answer some of the tantalizing open questions in the field.

Mots-clés : random surfaces,effective interface models,random operators,reinforced random walks,localization,long range order,rigidity in high dimensions,proper colorings,hard core model,o n  model
Projet ERC : VeriPhIED (Horizon 2020, appel 2020, 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 : 1499183.0 €

Measures against global warming require disruptive changes in the electricity sector. Drastically reducing CO2 emissions involves replacing bulk generation units with millions of renewable energy sources, along with a rapid increase of electricity demand. Maintaining the stability of the system with current approaches becomes not only computationally intractable, but also extremely costly. Recently proposed data-driven methods have been shown to handle the sheer complexity and have an impressive performance, achieving higher accuracy while being 250-1000 times faster than traditional methods. However, power systems are safety-critical systems, where data-driven methods will never be applied if they remain a black-box. This proposal removes the barriers for the application of data-driven approaches in power system problems, proposing methods that exploit the underlying physical properties of power systems. We propose the development of physics-aware verifiable neural networks and a neural network training procedure that can supply by-design guarantees of the neural network prediction accuracy. Accuracy does no longer need to be a statistical metric. Instead, our methods can supply a provable upper bound of the prediction error over the whole input space, that the power system operators can trust. We further show how neural networks can capture non-linear constraints impossible to capture before, and can reduce any non-linear optimization problem to a tractable mixed-integer linear program with verified accuracy, potentially boosting computation speed and tractability. From a power systems context, this enables us to treat power system dynamics and optimization in a unified framework that accurately captures the true feasible region, removes various approximations, and eliminates redispatching costs, saving billions of euros per year. The proposed methods naturally extend beyond power systems, finding application to a wide range of physical safety-critical systems.
Projet ERC : MEImpact (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
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 : 680682.25 €

Measuring economic activity is a fundamental challenge for empirical work in economics. Most empirical projects raise concerns about whether the data do in fact measure what they purport to measure. Mismeasurement may lead to severe model misspecification, biased estimates, and misled conclusions and policy decisions. Unfortunately, formally accounting for the possibility of mismeasurement in the econometric model is complicated and possible only under strong assumptions that limit the credibility of resulting conclusions. Therefore, the most common approaches to measurement issues are to ignore them, to informally argue why they may not be of first-order importance, to abandon the project, or to search for better data. The objective of the research described in this proposal is to develop new methodologies for formally assessing the potential impact of measurement error (ME) on all aspects of an empirical project: on model-building, on estimation and inference, and on decision-making. For instance, the new inference procedures allow the researcher to test whether ME is a statistically significant feature that should be modeled, whether ME distorts objects of interest (e.g. a production or utility function), whether ME distorts conclusions from hypothesis tests, and whether ME affects subsequent decision-making. I show that answering such questions is possible under much weaker assumptions than identification and estimation of a ME model and thus leads to more credible and robust conclusions. In addition, the implementation of the new procedures can be based on standard nonparametric estimation techniques that are part of many applied researchers’ toolkits. In consequence, the research has the potential to fundamentally transform the way empirical researchers approach measurement issues, to significantly impact empirical practice, and ultimately to avoid misled conclusions and policy decisions.

Mots-clés : measurement error,nonparametric methods,hypothesis testing,economic modeling,specification testing,robust decision making,economic frictions
Projet ERC : MARTE (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Social World and Its Diversity
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 : 1398553.0 €

Medically Assisted Reproduction (MAR) is one of the most important achievements of medical science in the last generation. In advanced societies, the number of MAR treatments increases every year, and, over the last four decades, more than five million MAR conceived children were born, and many more families received treatment. Given this trend, it is a public health prerogative to find out whether MAR affects the well-being of families. Prior findings are mixed and often hampered by low statistical power or conceptual limitations. I propose a programme of research that goes beyond the state-of-the-art by being the first to analyze comprehensively the effects of MAR on children, adults, and families through a combination of uniquely rich data, previously unused research designs, and conceptual innovations. First, in contrast to past work using small or convenience samples, I use extremely detailed and large datasets from population registers and surveys. Second, I compare the impact of MAR on different domains of life by analysing its effects on a range of adult and child outcomes (e.g., physical/mental health, education, union stability), thereby allowing me to investigate trade-offs that have not been previously tested. Third, I use innovative research designs to test whether the impact of MAR is causal by comparing children conceived through MAR treatments to their spontaneously conceived siblings, and adults who successfully conceive through MAR to those who are unsuccessful. The project has the potential to produce ground-breaking results that will impact future research in this field. Moreover, the project will have important policy implications, as its findings will be immediately relevant to health professionals advising couples seeking MAR treatments, to public health authorities allocating resources to mitigate the potentially negative effects of MAR on health, and to policy-makers considering whether to (further) subsidize MAR treatments.
Projet ERC : HighResCells (Horizon 2020, appel 2018, Synergy grants)
Entité participante : Spanish National Research Council — Pays : Espagne — Rôle : PI
Acronyme de l'entité : CSIC
Type d'entité : Recherche
Financement ERC de l'entité : 1726457.0 €

Members of the Epidermal Growth Factors Receptor family (EGFRs) influence cell growth and proliferation, and are pivotal in all phases of tumor progression. We will use this receptor family as an example with which to develop a ground-breaking new technology to study cellular signaling towards atomic resolution, in situ. Therefore, we propose to employ an interdisciplinary approach for studying EGFR family of receptors, where we follow their conformational and oligomeric states as well as bound ligands and signal transduction molecules during different activation states at atomic resolution in situ. We will progress from engineered to native receptor forms, and from defined membrane vesicles to whole cells, and employ 3D structure analysis by cryo-electron tomography, greatly enhanced by novel image processing approaches, mass spectroscopy definitions of receptor modifications and interaction partners, as well as advanced protein engineering to identify, orient and freeze receptors for this method development. This collaborative project addresses the properties of the EGFR family across a wide range of complexity and dimensions, in the cellular environment, through their high-resolution structures and changes during receptor recycling. This collaborative network, addressing EGFR from complementary angles, is most likely to generate substantial new information on these assemblies and to yield a deep understanding of the mechanisms underlying their structure and function. The EGFR family has been the focus of many tumor therapies, with the aim of intercepting their signaling, and this project will contribute to a more detailed understanding of their mode of action and thus the more rational development of such therapies in the future. However, the technology that will be developed will be generally applicable and may thus help to contribute to a paradigm change for structural biology, enabling atomic resolution description of receptors in their cellular environment.

Mots-clés : cryo electron tomography,protein engineering,protein design,organelle proteomics,egfr family
Projet ERC : HighResCells (Horizon 2020, appel 2018, Synergy grants)
Entité participante : University of Zurich — Pays : Suisse — Rôle : CO-PI
Acronyme de l'entité : UZH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 4774500.0 €

Members of the Epidermal Growth Factors Receptor family (EGFRs) influence cell growth and proliferation, and are pivotal in all phases of tumor progression. We will use this receptor family as an example with which to develop a ground-breaking new technology to study cellular signaling towards atomic resolution, in situ. Therefore, we propose to employ an interdisciplinary approach for studying EGFR family of receptors, where we follow their conformational and oligomeric states as well as bound ligands and signal transduction molecules during different activation states at atomic resolution in situ. We will progress from engineered to native receptor forms, and from defined membrane vesicles to whole cells, and employ 3D structure analysis by cryo-electron tomography, greatly enhanced by novel image processing approaches, mass spectroscopy definitions of receptor modifications and interaction partners, as well as advanced protein engineering to identify, orient and freeze receptors for this method development. This collaborative project addresses the properties of the EGFR family across a wide range of complexity and dimensions, in the cellular environment, through their high-resolution structures and changes during receptor recycling. This collaborative network, addressing EGFR from complementary angles, is most likely to generate substantial new information on these assemblies and to yield a deep understanding of the mechanisms underlying their structure and function. The EGFR family has been the focus of many tumor therapies, with the aim of intercepting their signaling, and this project will contribute to a more detailed understanding of their mode of action and thus the more rational development of such therapies in the future. However, the technology that will be developed will be generally applicable and may thus help to contribute to a paradigm change for structural biology, enabling atomic resolution description of receptors in their cellular environment.

Mots-clés : cryo electron tomography,protein engineering,protein design,organelle proteomics,egfr family
Projet ERC : HighResCells (Horizon 2020, appel 2018, Synergy grants)
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 : 1772500.0 €

Members of the Epidermal Growth Factors Receptor family (EGFRs) influence cell growth and proliferation, and are pivotal in all phases of tumor progression. We will use this receptor family as an example with which to develop a ground-breaking new technology to study cellular signaling towards atomic resolution, in situ. Therefore, we propose to employ an interdisciplinary approach for studying EGFR family of receptors, where we follow their conformational and oligomeric states as well as bound ligands and signal transduction molecules during different activation states at atomic resolution in situ. We will progress from engineered to native receptor forms, and from defined membrane vesicles to whole cells, and employ 3D structure analysis by cryo-electron tomography, greatly enhanced by novel image processing approaches, mass spectroscopy definitions of receptor modifications and interaction partners, as well as advanced protein engineering to identify, orient and freeze receptors for this method development. This collaborative project addresses the properties of the EGFR family across a wide range of complexity and dimensions, in the cellular environment, through their high-resolution structures and changes during receptor recycling. This collaborative network, addressing EGFR from complementary angles, is most likely to generate substantial new information on these assemblies and to yield a deep understanding of the mechanisms underlying their structure and function. The EGFR family has been the focus of many tumor therapies, with the aim of intercepting their signaling, and this project will contribute to a more detailed understanding of their mode of action and thus the more rational development of such therapies in the future. However, the technology that will be developed will be generally applicable and may thus help to contribute to a paradigm change for structural biology, enabling atomic resolution description of receptors in their cellular environment.

Mots-clés : cryo electron tomography,protein engineering,protein design,organelle proteomics,egfr family
Projet ERC : MIMO (Horizon 2020, appel 2014, Proof of concept grants)
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 : 150000.0 €

Mercury (Hg) is a toxic element with adverse effects on humans and wildlife. Cross-border export of atmospheric Hg emissions makes Hg a global pollutant. In 2013 the UNEP Minamata Convention on Mercury was signed by over 100 countries and aims to reduce future Hg emissions. Implementation of environmental Hg policy requires environmental monitoring efforts. Hg monitoring networks such as GMOS and AMNet typically gather data on atmospheric Hg concentrations, but do not provide information on the Hg emission source. In the ERC StG MERCURY ISOTOPES we have developed dual Hg isotope signatures as fingerprints for Hg sources and transformations. Our observations suggest that most, but not all, anthropogenic Hg emission sources are isotopically distinguishable. The goal of the ERC POC MIMO project is to develop, together with Tekran Inc., the largest global Hg instrument developer, a commercial Mercury Isotope MOnitor (MIMO) that is compatible with current Hg monitoring equipement and programs. Successful development of the MIMO will generate (a) patent(s), drive sales of MIMO and consumables, opens up worldwide opportunities for environmental monitoring companies, and addresses a globally important societal issue.

Mots-clés : mercury,environment,emissions,monitoring,commercial,isotopes,sources,anthropogenic
Projet ERC : NICHOIDS (Horizon 2020, appel 2016, Proof of concept grants)
Entité participante : National Research Council — Pays : Italie — Rôle : Partner
Acronyme de l'entité : CNR
Type d'entité : Recherche
Financement ERC de l'entité : 40000.0 €

Mesenchymal stem cells (MSCs) raise a great interest for regenerative medicine for many clinical applications such as orthopaedic, plastic and reconstructive surgery, within autoimmune or degenerative disease treatment and as immune-suppressive agents in organ transplantation. In stem cell therapy, cells are injected in the patient after an extensive in vitro manipulation aimed at obtaining a sufficient cell number able to guarantee the therapeutic effect. Currently, stem cell "manufacturing" implies the use of “feeder” cells and additives from animal sources, hampering the clinical use mainly for safety reasons. In the context of an ERC Consolidator grant (ERC-CoG) that I currently lead, I introduced a novel ground-breaking concept for an easy to use, repeatable, cost-effective, and safe substrate for stem cell expansion, called the “nichoid” substrate, capable of avoiding feeder cells and dangerous additives. The substrate is polymerized with a laser in a biocompatible resin using a frontier nano-fabrication technology called “two-photon laser polymerization” (2PP). To explore the possibility of an economic exploitation of this invention covered by an Italian Patent submitted in September 2015 and already extended as PCT, the goal of this PoC is to perform a technical and commercial feasibility to move the method developed in the PI’s laboratory during the ERC grant to the market. In particular, I plan to speed up the maturity level of the technology further developing the production process of individual nichoids (fabrication up-scaling by at least a factor of 10, allowing to cover a culture surface of 5 squared cm in five hours), setting of an actionable IPR strategy (including a market assessment), identification of the suitable exploitation strategy for valorising the patent/know how (licensing). The nichoid could drastically reduce down to few years the time to market of stem cell products that is currently 20 years.

Mots-clés : stem cells,adult,mesenchymal,cultivation,substrate,nanofabrication,laser,two photon polymerization,parallelization,up scaling,clinical translation,stem cell therapy,cell factory