Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : HyperQ (Horizon 2020, appel 2019, Synergy grants)
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 : 3517485.0 €

Many of the most remarkable contributions of modern science to society have arisen from the interdisciplinary work of scientists enabling novel methods of imaging and sensing. Outstanding examples are nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) which have enabled fundamental insights in a broad range of sciences extending from Chemistry to the Life Sciences. However, the key challenge of NMR and MRI is their very low inherent sensitivity due to the weak nuclear spin polarisation under ambient conditions. This makes the extension of magnetic resonance to the nanoscale (small volumes) and to the observation of metabolic processes (low concentrations) impossible. HyperQ will address this challenge with the development of room-temperature quantum control of solid-state spins to increase nuclear spin polarisation several orders of magnitude above thermal equilibrium and thereby revolutionise the state-of-the-art of magnetic resonance. Essential for this development is the synergy of an interdisciplinary team of world leaders in quantum control and hyperpolarised magnetic resonance to enable the development of quantum control theory (“Quantum Software”), quantum materials (“Quantum Hardware”), their integration (“Quantum Devices”) and applications to biological and medical imaging (“Medical Quantum Applications”). HyperQ will target major breakthroughs in the field of magnetic resonance, which include chip-integrated hyperpolarisation devices designed to operate in combination with portable magnetic resonance quantum sensors, unprecedented sensitivity of bio-NMR at the nanoscale, and biomarkers of deranged cellular metabolism. The HyperQ technology will provide access to metabolic processes from the micron to the nanoscale and thereby insights into metabolic signatures of a broad range of disease such as cancer, Alzheimer and the mechanisms behind neurodegenerative disease. This will enable fundamentally new insights into the Life Sciences.

Mots-clés : nuclear magnetic resonance,magnetic resonance imaging,hyperpolarisation
Projet ERC : MoDynStruct (Horizon 2020, appel 2020, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Institute of Science and Technology Austria — Pays : Autriche — Rôle : PI
Acronyme de l'entité : ISTA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2389169.75 €

Many real-world data sets change continuously, but their enormous size prohibits frequent re-processing of the whole data. Thus, there is an urgent need for efficient, fully dynamic data structures that maintain properties of the data set while supporting fast insertions and deletions. This is especially important for problems in data mining and network analysis, where a data structure often needs to fulfill new additional constraints that are not supported by classic data structures: (1) It should only use sublinear space, even if this leads to some small error in the answers. (2) As data sets frequently contain private information which needs to be protected, it should reveal nothing about individual data points, which is often modeled through differential privacy. Our ambitious goal is to design such groundbreaking new fully dynamic data structures for central problems on graphs and point sets. Specifically, we will focus on problems with large practical relevance such as subgraph detection, k-core decomposition, and balanced graph partitioning as well as various clustering variants in general metric spaces. For these problems no fully dynamic data structures with small asymptotic running time are known and they have not even been studied in the small-space or differentially-private regime. However, using recent advanced in algorithms research it is now the right time to develop novel techniques to solve these challenging questions. Thus, the goal of this project is to design algorithms for highly-relevant problems as well as advancing the field of data structures in general by moving it from a narrow focus on asymptotic complexity to a broader set of modern requirements with the goal of bridging the gap that currently exists between theory and practice. As data structures are used by every computer program the impact of this work will be far-reaching. With over 30 years of experience in algorithms research the PI is in the unique position to do so.

Mots-clés : fully dynamic data structures
Projet ERC : MoDynStruct (Horizon 2020, appel 2020, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Vienna — Pays : Autriche — Rôle : Partner
Acronyme de l'entité : Univie
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 88201.25 €

Many real-world data sets change continuously, but their enormous size prohibits frequent re-processing of the whole data. Thus, there is an urgent need for efficient, fully dynamic data structures that maintain properties of the data set while supporting fast insertions and deletions. This is especially important for problems in data mining and network analysis, where a data structure often needs to fulfill new additional constraints that are not supported by classic data structures: (1) It should only use sublinear space, even if this leads to some small error in the answers. (2) As data sets frequently contain private information which needs to be protected, it should reveal nothing about individual data points, which is often modeled through differential privacy. Our ambitious goal is to design such groundbreaking new fully dynamic data structures for central problems on graphs and point sets. Specifically, we will focus on problems with large practical relevance such as subgraph detection, k-core decomposition, and balanced graph partitioning as well as various clustering variants in general metric spaces. For these problems no fully dynamic data structures with small asymptotic running time are known and they have not even been studied in the small-space or differentially-private regime. However, using recent advanced in algorithms research it is now the right time to develop novel techniques to solve these challenging questions. Thus, the goal of this project is to design algorithms for highly-relevant problems as well as advancing the field of data structures in general by moving it from a narrow focus on asymptotic complexity to a broader set of modern requirements with the goal of bridging the gap that currently exists between theory and practice. As data structures are used by every computer program the impact of this work will be far-reaching. With over 30 years of experience in algorithms research the PI is in the unique position to do so.

Mots-clés : fully dynamic data structures
Projet ERC : QUNNECT (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed matter physics
Entité participante : Institute of Science and Technology Austria — Pays : Autriche — Rôle : PI
Acronyme de l'entité : ISTA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Many researchers in basic science and large IT companies are convinced that superconducting quantum processors will soon help solve complex problems faster, improve optimization and simulation, and boost the progress in artificial intelligence. A worldwide quantum web is the next logical step. It would not only improve communication security, it represents the key to unlock the full potential of the new quantum-computing paradigm. Unfortunately, research in optical quantum networks and superconducting devices has progressed largely independently so far. While superconducting qubits are ideally suited for on-chip integration and fast processing, they are problematic for quantum communication. In fact, no solution exists to connect remote qubits via a room temperature link. The small energy scales in the electrical circuit make the fragile information carriers (single microwave photons) susceptible to interference, thermal noise and losses, which has hindered any significant progress in this direction. Only just now we have gained sufficient insight into low loss materials, the required fabrication technology, and the precision measurement techniques necessary to bridge the two worlds, by controlling individual photons and phonons quantum coherently. We propose to integrate silicon photonics for low-loss fiber optic communication with superconducting circuits for quantum processing on a single microchip. As intermediary transducer we will focus on two approaches: (1) quantum ground state cooled nanoscale mechanical and (2) low-loss electro-optic nonlinear circuit elements. The novelty of our approach is the tight on-chip integration facilitated by the PIs interdisciplinary background in both, superconducting circuits and silicon nanophotonics. Integration will be the key for realizing a low-loss and high-bandwidth transceiver, for preparing remote entanglement of superconducting qubits, and for extending the range of current fiber optic quantum networks.

Mots-clés : superconducting circuits,circuit qed,optomechanics,electromechanics,electrooptics,quantum networks,fiber optics,quantum communication,silicon photonics
Projet ERC : NICHOID (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : Mario Negri Institute for Pharmacological Research — Pays : Italie — Rôle : Partner
Type d'entité : Recherche
Financement ERC de l'entité : 100500.0 €

Many therapeutic applications of stem cells require accurate control of their differentiation. To this purpose there is a major ongoing effort in the development of advanced culture substrates to be used as “synthetic niches” for the cells, mimicking the native ones. The goal of this project is to use a synthetic niche cell culture model to test my revolutionary hypothesis that in stem cell differentiation, nuclear import of gene-regulating transcription factors is controlled by the stretch of the nuclear pore complexes. If verified, this idea could lead to a breakthrough in biomimetic approaches to engineering stem cell differentiation. I investigate this question specifically in mesenchymal stem cells (MSC), because they are adherent and highly mechano-sensitive to architectural cues of the microenvironment. To verify my hypothesis I will use a combined experimental-computational model of mechanotransduction. I will a) scale-up an existing three-dimensional synthetic niche culture substrate, fabricated by two-photon laser polymerization, b) characterize the effect of tridimensionality on the differentiation fate of MSC cultured in the niches, c) develop a multiphysics/multiscale computational model of nuclear import of transcription factors within differentially-spread cultured cells, and d) integrate the numerical predictions with experimentally-measured import of fluorescently-labelled transcription factors. This project requires the synergic combination of several advanced bioengineering technologies, including micro/nano fabrication and biomimetics. The use of two-photon laser polymerization for controlling the geometry of the synthetic cell niches is very innovative and will highly impact the fields of bioengineering and biomaterial technology. A successful outcome will lead to a deeper understanding of bioengineering methods to direct stem cell fate and have therefore a significant impact in tissue repair technologies and regenerative medicine.

Mots-clés : mechanobiology,multiphysics,multiscale,mesenchymal,stem cell,differentiation,microfabrication,two photon polymerization,synthetic niche,substrate,cell culture
Projet ERC : MapCOM (Horizon 2020, appel 2015, Proof of concept grants)
Entité participante : Innovation Management Consulting Group (Sweden) — Pays : Suède — Rôle : Partner
Acronyme de l'entité : IMCG
Type d'entité : Organismes privés (hors enseignement)
Financement ERC de l'entité : 83750.0 €

MapCOM will transition the fundamental research carried out in the COOPNET project (the PI’s Starting Grant Project) to concrete applications related to power-efficient and scalable resource allocation for cellular networks, in particular for 5G communications. The key idea is to harness uncertain location information to build statistical radio maps, which can be used to predict radio signal quality and perform long-term proactive resource allocation. This will reduce operating costs for operators, provide better user experience for high-throughput applications, and enable new services and applications where low latency, reliable, and scalable communication is key (e.g., autonomous vehicles). Our COOPNET research has indicated the importance of explicitly accounting for various sources of uncertainty. We are now ready to establish the innovation potential of this research for commercial and societal benefit. Funded by the Western Sweden Innovation Office, we have already taken initial steps in this direction and have established possible innovation potential through meetings with Ericsson and Alcatel-Lucent, two equipment manufacturers. In this ERC Proof of Concept, we plan to demonstrate the technology and determine the market potential by: (i) confirming that the performance gains seen in theory also hold up in the real world; (ii) identifying possible market applications starting with telecom equipment manufacturers and telecom operators, but also other ICT industries. This analysis will allow us to identify, together with industrial actors, where the market value of the COOPNET concept lies. If successful, telecom equipment manufacturers and/or telecom operators will be able to provide the COOPNET functionality as part of their future products and services, giving them a significant advantage over competitors. We also expect this to be a first step towards multiple possible market applications to be identified in various areas of the ICT industries.

Mots-clés : 5g communication,crowd sourcing,radio mapping,network optimization
Projet ERC : CMBLENS (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
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 : 1492065.0 €

Maps of the gravitational lensing of the cosmic microwave background (CMB) directly trace the mass distribution across our universe. Though recent measurements have established the CMB lensing signal as a powerful probe of cosmology and fundamental physics, we are only beginning to exploit its scientific potential. My ERC-funded team will analyse lensing in CMB data from Advanced ACTPol and Simons Observatory, powerful ground-based experiments for which I am co-leading the lensing working groups. By developing and applying new analysis methods, my team will reconstruct large-sky CMB lensing maps that are an order of magnitude more precise than current state-of-the-art measurements. With our high-fidelity lensing maps, we will address key questions about the origin of the universe, the properties of neutrinos, the nature of dark energy, and the formation of cosmic structure. We will determine or tightly bound the unknown neutrino mass by measuring the CMB lensing power spectrum to 0.6% precision, place stringent constraints on the time evolution of dark energy by combining CMB lensing maps with large-scale structure datasets, and enable powerful searches for primordial gravitational waves by removing the limiting lensing signal from the CMB polarization. To achieve these goals, my group will address the central challenges posed by the advance of lensing science to ultra-high precision, pioneering new approaches to lensing estimation, foreground mitigation, and CMB B-mode delensing.
Projet ERC : ReAct (Horizon 2020, appel 2017, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Cultures and cultural production
Entité participante : Utrecht University — Pays : Pays-Bas — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2498750.0 €

Mass demonstrations are newsworthy. But how are they remembered when they are no longer news? Social movements are usually studied in terms of their emergence and subsidence. Despite recognition that activists are ‘inspired’ by precedents, the afterlife of activism in story and image has never been systematically explored. ReAct contends that knowledge about this cultural memory is needed for a full understanding civil resistance. ReAct will provide the first in-depth account of the remembering and forgetting of activism in Europe since the late 19th century. It will reveal continuities and changes in how protest has been depicted in different media regimes; demonstrate the role of texts, images, and commemorative practices in conveying the memory of protest to later generations; and show how this memory feeds back into later movements at home and abroad. The project is designed around case studies from periods of heightened activism in Europe: 1871-1914; 1960-1970; 2011-2012. Work packages follow 3 intersecting lines of inquiry: mediation (what cultural frames have been used to turn activism into transferable knowledge?); afterlives (how has the memory of particular movements been culturally transmitted?); memoryscapes (how have later movements referenced earlier ones?). A key innovation is network visualization to map the reproduction of narratives and references to predecessors. ReAct will effect a major reorientation in cultural memory research: by developing analytic tools with which to capture the cultural transmission of hope, it provides an alternative to the trauma-based models that currently dominate the field. It will also open up a new area of social movement research by revealing traditions of civic memory and how it is culturally produced. Outside of the academy, ReAct will provide critical literacies with which to rethink collective memory and identity in terms of active citizenship rather than ethnic-national grievances.

Mots-clés : cultural memory,activism,narrative,transmediality,transnationalism
Projet ERC : SysOrganoid (Horizon 2020, appel 2017, Consolidator grants)
Domaine scientifique : Life Science
Panel : Integrative Biology: from Genes and Genomes to Systems
Entité participante : Radboud University Nijmegen — Pays : Pays-Bas — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2000000.0 €

Mass spectrometry-based proteomics and next generation DNA sequencing emerged as two powerful and complementary technologies in biology. I was the first to integrate these technologies in the area of epigenetics to identify and functionally characterize proteins that interact with post-translational modifications on histones and (hydroxy)methylated DNA (so-called chromatin ‘readers’). My pioneering work revealed that an intricate networks of transcription factors, chromatin modifications and chromatin readers orchestrate dynamic gene expression programs during embryonic stem cell differentiation. The next big challenge is to understand the molecular mechanisms, which help to control maintenance and differentiation of adult stem cells as an integral part of an organ. Intestinal organoid cultures recently emerged as a paradigm to study adult stem cell maintenance and differentiation. These ‘miniguts’ can be cultured in vitro and contain all the different cell types that are present in the mouse small intestinal epithelium. Recently it was shown that small-molecule driven perturbations can be used to obtain organoids which are strongly enriched for specific intestinal cell types. This system thus provides a perfect opportunity to study for the first time and in a controlled manner, adult stem cell maintenance and (de)differentiation. Using small molecule-driven perturbations and a unique combination of ‘omics’ technologies, which are embedded in my department, I will provide a systems-wide view of the molecular (epigenetic) mechanisms that orchestrate cell fate changes in intestinal organoids. This integrative approach will identify the major regulatory networks that define the remarkable cellular plasticity of the mouse small intestinal epithelium. Beyond this basic scientific goal, our work will also have profound implications for cancer research and regenerative medicine, both of which are characterized by changes in adult stem cell homeostasis.

Mots-clés : interaction proteomics,organoids
Projet ERC : time-data (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
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 : 1931574.0 €

Massive data poses a fundamental challenge to learning algorithms, which is captured by the following computational dogma: The running time of an algorithm increases with the size of its input data. The available computational power, however, is growing slowly relative to data sizes. Hence, large-scale problems of interest require increasingly more time to solve. Our recent research demonstrates that this dogma is false in general, and supports an emerging perspective: Data should be treated as a resource that can be traded off with other resources such as running time. For data acquisition and communications, we have also shown related sampling, energy, and circuit area trade-offs. A detailed understanding of time-data and other analogous trade-offs, however, requires interdisciplinary studies that are currently in their infancy even for basic system models. Existing approaches are too specialized, and crucially, they only aim at establishing a trade-off, but not characterizing its optimality or its technological feasibility. TIME-DATA will confront these challenges by building unified mathematical foundations on how we generate data via sampling, how we set up learning objectives that govern our fundamental goals, and how we optimize these goals to obtain numerical solutions. We will demonstrate our rigorous theory with task-specific, end-to-end trade-offs (e.g., samples, power, computation, and statistical precision) in broad domains, by not only building prototype analog-to-information conversion hardware, but also accelerating scientific and medical imaging, and engineering new tools of discovery in materials science. Our goal of systematically understanding and expanding on this emerging perspective is ambitious: Our mathematical sampling framework, in tandem with new universal primal-dual algorithms and geometric estimators, are expected to change the way we treat data in information systems, promising substantial flexibility in the use of limited resources.

Mots-clés : convex optimization,statistical inference,information theory,array signal processing,analog to digital converters,medical imaging,fourier optics systems,materials discovery,super resolution