Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : COSMOS (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : Delft University of Technology — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : TU Delft
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499415.0 €

Modern society is based on large-scale, interconnected, complex infrastructures, e.g. power, transportation and communication systems, with network structure and interacting subsystems controlled by autonomous components and human users, generically called “agents”. These systems possess the features of “complex” systems of systems (C-SoS), such as rationality and autonomy of the agents, and require effective multi-agent coordination and control actions for their safe and efficient operation. Multi-agent optimization has attracted an extraordinary amount of research attention as a methodology to let agents cooperatively coordinate their actions, but it is inappropriate and ineffective for systems with noncooperative (selfish) agents, virtually all modern C-SoS. A paradigm shift is necessary to ensure safe and efficient operation of complex systems with possibly noncooperative agents. With this aim, COSMOS shall embrace dynamic game theory and pursue a twofold scientific and technical objective: 1) to conceive a unifying framework for the analysis and control of complex, multi-agent, mixed cooperative and noncooperative, systems; 2) to provide automated computational methods for solving coordination, decision and control problems in C-SoS. To achieve these goals, COSMOS will adopt a novel operator-theoretic approach, and integrate methods within and across dynamic game theory, networked multi-agent systems and control, statistical learning, stochastic and mixed-integer optimization.

Mots-clés : cyber physical systems of systems,distributed control,monotone game theory,scenario approach
Projet ERC : DistOpt-BydWorstCase (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
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 : 1540000.0 €

Modern systems are increasingly dezentralized and massively distributed computations play a vital role in the systems of the future. This project aims to advance the foundational aspects of distributed computing, particularly MessagePassing algorithms for optimization problems. The strong shift towards distributed systems also lead to a flurry of works on such algorithms -- many optimization problems now have distributed algorithms with optimal worst-case performance guarantees. Generally, these guarantees cannot be improved because they match unconditional impossibility results, which prove severe limitations on the performance of distributed algorithms in some (pathological) network topologies. Real world networks, however, are never worst-case and do not share the limiting bottleneck characteristics of these pathological topologies. In fact, there is no known barrier for ultra-fast polylogarithmic-round distributed algorithms on any network of interest. This leaves an exponential gap between current worst-case-optimal algorithms and what is likely possible in many, if not all, real-world settings. Motivated by this, this project provides a program for a general toolbox and theory for MessagePassing optimization algorithms that go beyond worst-case topologies. The main and guiding high-risk high-gain goal is the development of universally optimal distributed algorithms, which are competitive with the best algorithm on any given topology. This would constitute the strongest possible form of algorithmically adjusting to non-worst-case topologies. A detailed program with many concrete and smaller stepping stones towards this ambitious breakthrough objective is provided. Many of the novel questions stemming from this program and proposal are highly interdisciplinary, crossing boundaries between information theory, distributed computing, topological graph theory, and other parts of theoretical computer science, and are fundamental and interesting in their own right.

Mots-clés : theory of distributed computing,congest,messagepassing,minimum spanning tree problem,beyond worst case,universal optimality,global network optimization,network coding,network coding gaps
Projet ERC : RobustStats (Horizon 2020, appel 2020, Advanced 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 : 2050068.0 €

Modern technology allows large-scale data to be collected in many new forms, and their underlying generating mechanisms can be extremely complex. In fact, an interesting (and perhaps initially surprising) feature of large-scale data is that it is often much harder to feel confident that one has identified a plausible statistical model. This is largely because there are so many forms of model violation and both visual and more formal statistical checks can become infeasible. It is therefore vital for trust in conclusions drawn from large studies that statisticians ensure that their methods are robust. The RobustStats proposal will introduce new statistical methodology and theory for a range of important contemporary Big Data challenges. In transfer learning, we wish to make inference about a target data population, but some (typically, most) of our training data come from a related but distinct source distribution. The central goal is to find appropriate ways to exploit the relationship between the source and target distributions. Missing and corrupted data play an ever more prominent role in large-scale data sets because the proportion of cases with no missing attributes is typically small. We will address key challenges of testing the form of the missingness mechanism, and handling heterogeneous missingness and corruptions in classification labels. The robustness of a statistical procedure is intimately linked to model misspecification. We will advocate for two approaches to studying model misspecification, one via the idea of regarding an estimator as a projection onto a model, and the other via oracle inequalities. Finally, we will introduce new methods for robust inference with large-scale data based on the idea of data perturbation. Such approaches are attractive ways of exploring a space of distributions in a model-free way, and we will show that aggregation of the results of carefully-selected perturbations can be highly effective.

Mots-clés : 62d10 missing data,62f35 robustness and adaptive procedures,62g05,nonparametric estimation,62g35 nonparametric robustness,62h30,classification and discrimination,cluster analysis
Projet ERC : QUITAR (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed matter physics
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 : 2305265.0 €

Modern telecommunications networks make use of light in optical fibers to connect devices where information is processed in electronic circuits. Such an architecture can also be used to communicate and process quantum information. Local quantum processors based on superconducting (SC) microwave circuits are now capable of performing sophisticated tasks ranging from quantum simulation to quantum error correction. At the same time, low-loss quantum channels based on infrared (IR) light can transfer quantum information over long distances. However, the crucial link between these two systems that would allow for the realization of a quantum network is still missing. Since quantum states are much more fragile than classical signals, quantum transduction between the electrical and optical domains must be highly efficient without introducing new sources of decoherence that interfere with the operation of the local processors or long-distance channels. The goal of this project is to create a quantum transducer between SC circuits and IR light using a third quantum system: sound waves in an acoustic resonator. My recent work showed that these mechanical resonators possess properties that make them highly promising for implementing a quantum transducer. They couple efficiently to both SC circuits and IR light, and can be used to store and manipulate quantum states of sound. The project will combine electromechanical and optomechanical transduction, which so far has only been implemented separately, in a single system. By developing techniques for integrating optics, acoustics, and microwave circuits at cryogenic temperatures, I will demonstrate the conversion of complex quantum states between the microwave and IR domains and use this capability to entangle remote SC quantum nodes. Reaching this goal will be the crucial first step toward using SC circuits to implement a quantum network for long-distance communications or to build a large-scale, modular quantum computer.

Mots-clés : optomechanics,quantum transduction,superconducting circuits,brillouin scattering,acoustic resonators
Projet ERC : ArtMotor (Horizon 2020, appel 2020, Synergy grants)
Entité participante : Lund University — Pays : Suède — Rôle : CO-PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 3496802.0 €

Molecular motors and machines are essential for all cellular processes that together enable life. Built from proteins, with a wide range of properties, functionalities and performance characteristics, biological motors perform complex tasks and can transduce chemical energy into mechanical work more efficiently than human-made combustion engines. Sophisticated studies of biological protein motors have led to much structural and biophysical information and the development of models for motor function. However, from the study of highly evolved, biological motors it remains difficult to discern detailed mechanisms, for example about the relative role of different force generation mechanisms, or how information is communicated across a protein to achieve the necessary coordination. A promising, complementary approach to answering these questions is to build synthetic protein motors from the bottom up. Indeed, much effort has been invested in functional protein design, but so far, the ‘holy grail’ of designing and building a functional synthetic protein motor has not been realized. The purpose of ArtMotor is to design and build functional, synthetic protein motors capable of moving and transducing energy, based on existing, non-motor protein modules of known molecular function. Harnessing the synergy of expertise in computational protein design, structural and molecular biology, and single-molecule detection, we will use a two-pronged approach to (a) construct relatively simple protein motors that will require external control, while (b) construct, step by step, an autonomous protein motor capable of moving along a track. Such a functional, synthetic protein will constitute a ground-breaking advance in synthetic biology, physics and engineering. In addition to gaining new insights into mechanisms of energy transduction in proteins, we will also inspire other, complex protein designs that may lead to advances in fields from enzyme design to nano-engineering.

Mots-clés : protein design,protein engineering,artificial molecular motor,molecular motor,molecular machine,synthetic biology,single molecule approaches
Projet ERC : AMSEL (Horizon 2020, appel 2015, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : IBM Research - Zurich — Pays : Suisse — Rôle : PI
Type d'entité : Organismes privés (hors enseignement)
Financement ERC de l'entité : 2000000.0 €

Molecular structure elucidation is of great importance in synthetic chemistry, pharmacy, life sciences, energy and environmental sciences, and technology applications. To date structure elucidation by atomic force microscopy (AFM) has been demonstrated for a few, small and mainly planar molecules. In this project high-risk, high-impact scientific questions will be solved using structure elucidation with the AFM employing a novel tool and novel methodologies. A combined low-temperature scanning tunneling microscope/atomic force microscope (LT-STM/AFM) with high throughput and in situ electrospray deposition method will be developed. Chemical resolution will be achieved by novel measurement techniques, in particular the usage of different and novel tip functionalizations and combination with Kelvin probe force microscopy. Elements will be identified using substructure recognition provided by a database that will be erected and by refined theory and simulations. The developed tools and techniques will be applied to molecules of increasing fragility, complexity, size, and three-dimensionality. In particular samples that are challenging to characterize with conventional methods will be studied. Complex molecular mixtures will be investigated molecule-by-molecule taking advantage of the single-molecule sensitivity. The absolute stereochemistry of molecules will be determined, resolving molecules with multiple stereocenters. The operation of single molecular machines as nanocars and molecular gears will be investigated. Reactive intermediates generated with atomic manipulation will be characterized and their on-surface reactivity will be studied by AFM.
Projet ERC : MaMoGH (Horizon 2020, appel 2020, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The study of the human past
Entité participante : University of Birmingham — Pays : Royaume-Uni — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 833523.81 €

Monocultures are one of the great projects of industrial modernity. From the corn fields of Iowa to date palms in the Sahara, from banana plantations in the Americas to eucalyptus trees in India – wherever we look, we find large production regimes that focus on a single commodity. They supply the lion’s share of the world’s food and create a plethora of economic, social, political and environmental problems. However, there is a great paradox behind the global hegemony of monocultures: they thrive in the absence of a clear paradigm. There is no biological theory of monoculture – and plenty of evidence for the benefits of biological diversity. The project brings historical research to bear in a field that is traditionally the province of agronomists and activists. In the best tradition of global history, it reveals what isolated case studies have failed to see: problems, solutions and actors were surprisingly similar in otherwise different monocultures around the world. Based on carefully selected case studies and intensive communication with experts from forestry and the agricultural sciences, the project identifies the common patterns in the trajectories that monocultures followed around the world and uses that for a new interpretation of the growth and resilience of monocultures that only a historian can provide: a global narrative of recurring crises, of externalizing costs, and of an ever-growing path dependency. Monocultures are stumbling on in spite of the costs because the price of reverting course seems even greater. Outputs include five monographs, culminating in a path-breaking synthesis. The project seeks to challenge existing boundaries between historical subdisciplines and explore the common ground between agricultural history, forest history, environmental history, economic history, and the history of science and technology. It also aims to make history relevant in a 21st-century world where organic production will face unprecedented challenges.

Mots-clés : environmental history,agricultural history,forest history
Projet ERC : PLAST-EVOL (Horizon 2020, appel 2017, Advanced grants)
Domaine scientifique : Life Science
Panel : Environmental Biology, Ecology and Evolution
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 : 2499986.0 €

More than 1 Ga ago, an ancient eukaryote became photosynthetic by engulfing a cyanobacterium that evolved into the first plastid. This had profound consequences for the planet: photosynthetic eukaryotes became major primary producers and biogeochemical players. From this primary endosymbiosis evolved green algae and plants, red algae, and glaucophytes. Later, secondary endosymbioses of red and green algae spread plastids in numerous other phyla. However, many aspects of this evolutionary history remain obscure. The identity of that cyanobacterium was a mystery until we recently found strong phylogenomic evidence that it belonged to the Gloeomargaritales, a newly discovered deep-branching group. Yet, why this original endosymbiosis in a heterotrophic host was so successful is unknown. I hypothesize that it was due to the unusual capacity of Gloeomargaritales to produce intracellular carbonates which, I argue, allowed buffering the pH increase concomitant with photosynthetic C fixation. This was crucial for photosynthesis-based endosymbiosis before the host evolved active pH regulation. Similar intracellular amorphous carbonates recently found in green algae might be modern remnants of that initial symbiosis. To test this hypothesis and gain insight into the origin and subsequent evolution of plastids, I propose to: 1) thoroughly characterize the physiology of Gloeomargarita lithophora, the only Gloeomargaritales species isolated so far; 2) obtain new genomic data from diverse Gloeomargaritales by culture, metagenomic and single-cell approaches; 3) study the impact of cyanobacterial genes endosymbiotically transferred to the host algal and plant nuclear genomes; and 4) retrace the evolutionary history of these genes in secondary and tertiary endosymbioses. My project will improve our understanding of the origin and evolution of photosynthetic eukaryotes, and influence broader evolutionary biology research areas, from endosymbiosis to the eukaryotic tree of life.

Mots-clés : plastids,algae,endosymbiosis
Projet ERC : IMCIS (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : Technical University of Munich — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : TUM
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1495906.0 €

More than 100 million EU citizens suffer from chronic inflammatory skin diseases such as psoriasis and atopic eczema (AE). The diseases imply a devastating life quality similar to that of cancer, and cause direct socio-economic costs in the magnitude of 100 billion Euro each year in the EU. Despite all efforts, psoriasis and AE remain undertreated and the concept of individualised (also called precision) medicine could not be established in the field. Consequently, intensified research is demanded by organisations such as the WHO. Unmet medical needs are 1) a diagnostic gap, 2) lack of prediction possibilities to define the optimal therapy for an individual patient, and 3) a substantial number of non-responders to therapies. Reasons for these shortcomings are the heterogeneity of both psoriasis and AE and insufficient collaboration of clinical specialists, basic researchers, and bio-informaticians. This proposal aims at improving health care of inflammatory skin diseases by implying the concept of individualised medicine. The crucial step towards this goal is the ground-breaking idea to link deep clinical phenotyping to molecular signatures in lesional skin. Deep phenotyping means each patient is characterised by 86 clinical, histological, and laboratory attributes rather than the imprecise state-of-the art approach of rough diagnosing. Each attribute gets assigned to molecular events in lesional skin. Gene regions as well as key pathogenic molecules are identified in a novel gene network of inflamed skin, referred to as BRAIN (biological relevance assigned intelligent network). Candidate targets get validated using state-of-the-art cell culture systems and full skin models. This innovative and ambitious approach will substantially improve our knowledge of the pathogenesis and primary triggers of both psoriasis and AE, safe European health care systems direct costs in the magnitude of 10 billion Euro, and have a model character for complex diseases in general.

Mots-clés : individualised medicine
Projet ERC : HydroSocialExtremes (Horizon 2020, appel 2017, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : Uppsala University — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1835361.0 €

More than 100 million people per year are affected by hydrological extremes, i.e. floods and droughts. Hydrological studies have investigated human impacts on droughts and floods, while conversely social studies have explored human responses to hydrological extremes. Yet, the dynamics resulting from their interplay, i.e. both impacts and responses, have remained poorly understood. Thus, current risk assessment methods do not explicitly account for these dynamics. As a result, while risk reduction strategies built on these methods can work in the short-term, they often lead to unintended consequences in the long-term. As such, this project aims to unravel the mutual shaping of society and hydrological extremes. A combined theoretical and empirical approach will be developed to uncover how the occurrence of hydrological extremes influences society’s wealth, institutions and population distribution, while, at the same time, society in turn alters the frequency, magnitude and spatial distribution of hydrological extremes via structural measures of water management and disaster risk reduction. To explore the causal mechanisms underlying this mutual shaping, this project will propose explanatory models as competing hypotheses about the way in which humans drive and respond to droughts and floods. These alternative explanations will be developed and tested through: i) empirical analysis of case studies, and ii) global investigation of numerous sites, taking advantage of the current unprecedented proliferation of worldwide datasets. By combining these different methods, this project is expected to address the gap of fundamental knowledge about the dynamics of risk emerging from the interplay of hydrological extremes and society.