Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : SURFINK (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : Donostia International Physics Center — Pays : Espagne — Rôle : PI
Acronyme de l'entité : DIPC
Type d'entité : Recherche
Financement ERC de l'entité : 1894723.0 €

With the advent of self-assembly, increasingly high hopes are being placed on supramolecular materials as future active components of a variety of devices. The main challenge remains the design and assembly of supramolecular structures with emerging functionalities tailored according to our needs. In this respect, the extensive research over the last decades has led to impressive progress in the self-assembly of molecular structures. However, self-assembly typically relies on non-covalent interactions, which are relatively weak and limit the structure’s stability and often even their functionality. Only recently the first covalently bonded organic networks were synthesized directly on substrate surfaces under ultra-high-vacuum, whose structure could be defined by appropriate design of the molecular precursors. The potential of this approach was immediately recognized and has attracted great attention. However, the field is still in its infancy, and the aim of this project is to lift this new concept to higher levels of sophistication reaching real functionality. For optimum tunability of the material’s properties, its structure must be controlled to the atomic level and allow great levels of complexity and perfection. Complexity can be reached e.g. with hybrid structures combining different types of precursors. In this project, this hardly explored approach will be applied to three families of materials of utmost timeliness and relevance: graphene nanoribbons, porous frameworks, and donor-acceptor networks. Along the pursuit of these objectives, side challenges that will be addressed are the extension of our currently available chemistry-on-surfaces toolbox by identification of new reactions, optimized reaction conditions, surfaces, and ultimately their combination strategies. A battery of tools, with special emphasis on scanning probe microscopies, will be used to visualize and characterize the reactions and physical-chemical properties of the resulting materials.

Mots-clés : on surface chemistry,bottom up synthesis of functional materials,graphene nanoribbons,heterogeneous catalysis,donor acceptor networks,photovoltaic processes,self assembly
Projet ERC : HYPATIA (Horizon 2020, appel 2018, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Institut national de recherche en sciences et technologies du numérique — Pays : France — Rôle : PI
Acronyme de l'entité : Inria
Type d'entité : Recherche
Financement ERC de l'entité : 2223779.0 €

With the ever-increasing use of internet-connected devices, such as computers, smart grids, IoT appliances and GPS-enabled equipments, personal data are collected in larger and larger amounts, and then stored and manipulated for the most diverse purposes. Undeniably, the big-data technology provides enormous benefits to industry, individuals and society, ranging from improving business strategies and boosting quality of service to enhancing scientific progress. On the other hand, however, the collection and manipulation of personal data raises alarming privacy issues. Both the experts and the population at large are becoming increasingly aware of the risks, due to the repeated cases of violations and leaks that keep hitting the headlines. The objective of this project is to develop the theoretical foundations, methods and tools to protect the privacy of the individuals while letting their data to be collected and used for statistical purposes. We aim in particular at developing mechanisms that: (1) can be applied and controlled directly by the user, thus avoiding the need of a trusted party, (2) are robust with respect to combination of information from different sources, and (3) provide an optimal trade-off between privacy and utility. We intend to pursue these goals by developing a new framework for privacy based on the addition of controlled noise to individual data, and associated methods to recover the useful statistical information, and to protect the quality of service.

Mots-clés : local differential privacy,convex optimization,information theory
Projet ERC : IoN (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : IMEC — Pays : Belgique — Rôle : Partner
Type d'entité : Recherche
Financement ERC de l'entité : 265130.0 €

With the increase of people suffering from various neural disorders, the need for brain-computer interfaces (BCIs) to regain sensory-motor or cognitive functions are expected to become acute in the coming decades. However, the existing BCIs can only control simple motions, e.g., grasping, and are far from realizing our vision to help paralyzed patients to walk again. This is due to the lack of a high-bandwidth wireless BCI, capable of supporting the recording from a large number of neurons with high spatial and temporal resolution, while having large spatial coverage, brain-wide. In IoN, we target to achieve a breakthrough in the ability to transfer data from intracortical recording devices, e.g., multi-electrode arrays, by developing a transcranial telemetry system that enables the efficient transfer at high data rate from such high channel count sensors (e.g., imec’s Neuropixels with 1000 channels). Most importantly, it will also fulfil the form factor required for minimally-invasive surgery, needed to minimize the surgical risk and the complications after insertion. Furthermore, IoN will significantly scale up brain-wide recordings, by introducing a new telemetry network that has the capacity to support 16 distributed recording nodes (enabling a total of 16,000 channels), which has never been demonstrated from any BCIs before. To reach these challenging targets, we propose i) a novel hybrid signal propagation method to achieve a 500Mbps data rate with a 10mm^2 implant area, 20× smaller than the state-of-the-art; ii) a completely new “spike-Aloha” protocol to maximize the network capacity, supporting 166× more channels. The technology developed in IoN will be an important transformational step to revolutionize the way neuroscientists and neurologists collect and process brain-wide neural data. By introducing this miniature, energy-efficient, and high-capacity wireless telemetry network, we want to help patients with disability to regain the quality of life.

Mots-clés : brain computer interfaces,wireless communications,wireless networks,cmos asic design,implantable electronics,low power electronics,integrated micro systems,impulse radio ultra wideband
Projet ERC : DistMaP (Horizon 2020, appel 2019, 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 : Partner
Acronyme de l'entité : ETH Zurich
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 706250.0 €

With the rapidly growing size of the data and the pervasiveness of distributed systems and networks, it is a certainty that distributed and parallel computations will play a vital role in the computations of the future. This project aims to advance our understanding of the foundational aspects of these areas. We tackle some of the central questions in distributed algorithms and massively parallel algorithms for graph problems, which require us to go well-beyond the current state of the art. Our research plan involves three directions: - Developing efficient and particularly polylogarithmic-time deterministic distributed algorithms for some of the central graph problems of the area. Our hope is to do this through a general derandomization method that removes the randomness from efficient randomized algorithms. This question underlies some of the well-known open problems of the area. - Developing improved and particularly sublogarithmic-time randomized distributed algorithms for some of the central local graph problems of the area, thus hopefully narrowing or ideally closing this decade old gap to the respective lower bounds. - Developing improved massively parallel algorithms for some of the fundamental graph problems, with a special focus on the challenging regime of lower memory machines, which remains widely open. Given the high risk nature of these questions, in each direction, besides our plan of attack on the bigger and more ambitious objectives, we also explain a number of smaller problems, which should be more feasible, and which would serve as stepping stones toward the bigger goal. Moreover, we are hopeful that the simultaneous study of distributed algorithms and massively parallel will lead to a strengthening of the connections between these two areas and would also bring the related scientific communities closer to each other.

Mots-clés : distributed graph algorithms,massively parallel graph algorithms,local algorithms,theory of distributed computing
Projet ERC : VITAE (Horizon 2020, appel 2018, 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 : 137238.0 €

With the recent discovery that brain blood flow decreases — in a statistical and epidemiologic sense — before accumulation of neurotoxic deposits (e.g. amyloid and tau) and before any measurable cognitive deficits, the brain vascular system is increasingly recognized as a key player in the development of Alzheimer’s Disease (AD). This opens the way for the development of new disease-modifying therapeutic strategies targeting vascular pathways at early stages of AD, i.e. long before the neurodegenerative process becomes established and symptomatic. The central idea of this POC proposal is that High Performance Computing (HPC) assessment of cerebrovascular function from quantitative vessel network data will tremendously accelerate vascular-targeted drug development for neurodegenerative brain diseases. One of the key outputs of the research conduced in the ERC Consolidator BrainMicroFlow is indeed a HPC platform for modeling blood flow, nutrient delivery and metabolic waste removal in large anatomical vessel networks of the brain. However, using this platform requires advanced and specific parallel programing skills, which are in general not available, neither in the research nor in the R&D industrial environments focused on Alzheimer Disease. Thus, our aims are 1/ to upgrade the technical specifications of this platform and to develop a user-friendly and versatile interface, documentation and support, to create a new software, called VITAE, meeting the industry standards, that users with minimal programming skills could use on their own or local computational resources; 2/ to optimize the HPC capacities of VITAE so that its performances can reach the scale of a whole mouse brain (VITAE4WholeBrain), that could be accessed as a service provided to end users by partnering computational centers; 3/ to assess the market and the competitors for both versions of VITAE; 4/ to determine IPR position and strategy, and 5/ to explore licensing and partnership possibilities.

Mots-clés : brain microcirculation,blood flow and mass transfers,high performance computing,software,in silico medicine,alzheimer s disease,cerebral blood flow,vascular targeted drug development,biomarkers
Projet ERC : MaMs2 (Horizon 2020, appel 2015, Proof of concept grants)
Entité participante : KTH Royal Institute of Technology — Pays : Suède — Rôle : PI
Acronyme de l'entité : KTH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Within the ERC-M&M´s Starting Grant project (No.277879) we are exploring an innovative platform technology for wafer-level heterogeneous integration of integrated circuits (ICs) with micro/nano-electromechanical systems (MEMS/NEMS). This type of heterogeneous 3D integration technology is part of the More-than-Moore paradigm depicted in the International Technology Roadmap for Semiconductors (ITRS) and allows the combination of standard CMOS-based integrated circuits and high-performance MEMS devices to form complex and highly integrated systems that are not possible to manufacture with other micro-manufacturing approaches. First heterogeneous integration platforms for IC-integrated MEMS products have already been commercialized with great success by InvenSense (USA) and by mCube (USA) in collaboration with TSMC (Taiwan), the world’s largest CMOS foundry. However, the established heterogeneous integration technologies have limitations with respect to the type of MEMS devices that can be realized and the extent to which the MEMS-ICs system component can be miniaturized. The heterogeneous integration platform developed in this ERC-M&M’s+2 Proof of Concept proposal is suitable for a variety of small-sized MEMS components with large-volume markets, including infrared (IR) detector arrays, micro-mirror arrays, ultra-low power circuits based on NEM switches (a specific target application are low-power FPGAs), gyroscopes, accelerometers, microphones, etc. Thus, the heterogeneous integration platform is addressing a potential multi-billion € market. In contrast to the competing technologies, our heterogeneous integration platform is enabling the integration of ICs and MEMS/NEMS devices with extremely small device dimensions in the nm-range, very small via dimensions in the sub-1μm-range and placement accuracies of the MEMS/NEMS components on the target ICs in the nm-range.

Mots-clés : heterogeneous mems integration,nems,mems foundry,ir imaging,nems switch
Projet ERC : EPOCHS (Horizon 2020, appel 2017, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : University of Manchester — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1951138.0 €

Within the first few hundred million years after the Big-Bang the first galaxies and stars were born. Sometime soon after, these first objects produced enough energetic photons to reionization the neutral gas in the universe. This frontier of early galaxy assembly has not yet been observed, but will be uncovered by deep imaging and spectroscopy taken with the James Webb Space Telescope (JWST). Key problems include: how the very first galaxies were assembled, and evolved, in their first few Gyr, and the history of reionization. With this ERC funded EPOCHS project I will lead a major effort to investigate these questions using JWST GTO time discovering galaxies before, during, and after the epoch of reionization. This proposal has three interconnected and complementary themes: (i) Identifying the first galaxies and characterizing their UV luminosities, stellar masses, and star formation rates at 7<z<12. JWST imaging and spectroscopy will allow us to make significant progress beyond the current state of the art, and to use these measures to test models of the earliest galaxy assembly. (ii) Using these galaxies we will map the process of reionization: the sources of it, and the time-scale of its onset and duration. Using new diagnostics we will address uncertainties that currently plague this calculation, including escape fractions and the number of ionizing photons, using UV emission lines, spectral shapes, and measuring hardness ratios with radiative transfer models. (iii) We will measure the rest-frame optical structures of galaxies at 3<z<7 to reveal the formation modes of galaxies when they assembled their first masses and structures. We will determine how and when compact galaxies, mergers, dissipative formation in star forming disks, and the formation of bulges and disks are occurring. This includes measuring the formation history of internal components in 3<z<7 galaxies, allowing us to examine how quenching is occurring ‘inside-out’ or ‘outside-in’.
Projet ERC : Hot-Coal WM (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Stockholm University — Pays : Suède — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 0.0 €

Working memory (WM) is a fundamental cognitive capability. It refers to our ability to hold, select and manipulate several objects in mind simultaneously. It allows us to engage in flexible behavior and is tightly linked to fluid intelligence. This project will answer an essential, yet unsolved aspect of WM: How can primates use their WM in a generalized way and control what they think about? If you hear ‘apple’, ‘stone’ and ‘pear’ in sequence, and then you are asked to imagine the first fruit, how is it that you do not confuse apples with pears? There are many competing models of WM, but no biologically detailed models are capable of generalization. Neural networks can be trained to perform similar WM tasks as primates do, a major difference is that primates generalize their training. They can learn the task on a set of objects, then perform it on a novel set. Computational models typically rely on changing the connections between units to achieve the desired activity patterns to solve the task. Since these activity patterns depend on the objects held in WM, the training does not translate to novel objects. I propose a new solution to this problem, the Hot-Coal model of WM. It relies on a novel computational principle in which spatial location of information, rather than connectivity, is controlled by excitatory bursts to support cognition. I will explore this principle and test it in data. Preliminary tests suggest that the Hot-Coal theory is supported by electrophysiological data from primates. By implementing the theory in computational networks I aim to demonstrate the generalization mechanism and provide more detailed predictions. Finally, I will use the theory to resolve seemingly conflicting findings regarding the mechanisms underlying WM, by reproducing them in a single model. The new theory could constitute a significant advance in the mechanistic understanding of one of the most central and puzzling components of cognition.

Mots-clés : working memory,computational neuroscience,generalization,systems neuroscience,computational cognition
Projet ERC : MEMCIRCUIT (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
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 : 1499989.0 €

Working memory is the basis of cognition. It allows behaviour to be governed by internal goals rather than by reflexive stimulus-response mappings. The neural mechanisms of memory maintenance are heavily contested. Animal electrophysiology studies suggest a pivotal role of persistent spiking activity in single neurons of the prefrontal cortex, whereas human neuroimaging points to sub-threshold synaptic activity distributed across sensory cortex. This double dissociation of storage mechanisms and storage sites could result from the comparison of different recording methods, which measure distinct neuronal signals. Alternatively, there might be a fundamental difference between humans and animals. My key objective is to formulate general principles of working memory coding at the cellular and circuit level by integrating across previously disconnected lines of research. I will address two central questions. First, where and how is the memorized information stored? I will use an innovative approach to record large-scale single-unit activity from a cognitive (prefrontal) and a sensory (auditory) region in awake neurosurgical patients and directly compare the data to recordings from mice performing the same auditory working memory task. This will allow me to determine whether the mnemonic fingerprints are species-specific or shared across humans and rodents. Second, what neural pathways support the flow of information during working memory? Using the animal model, I will disrupt cross-regional interactions in the memory network with millisecond-precise, projection-specific optogenetic tools in order to dissect the contribution of each hub to memory storage and memory access. This project provides an unprecedented one-to-one matching of behavioural tasks and recording methods with single-cell and split-second resolution in humans and rodents. It represents a major step forward in understanding the cellular and circuit basis of a critical cognitive brain function.

Mots-clés : human single unit recordings,working memory,prefrontal cortex
Projet ERC : XFab (Horizon 2020, appel 2017, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : National Research Council — Pays : Italie — Rôle : PI
Acronyme de l'entité : CNR
Type d'entité : Recherche
Financement ERC de l'entité : 1998785.0 €

Xenes denote two-dimensional (2D) monoelemental (X) crystals beyond graphene with a honeycomb lattice. Unlike graphene, Xenes do not exist in Nature, but they become stable via epitaxy on substrates. So far experimental evidences of Xene epitaxy have been reported for X=Si, Ge, Sn, B, P, and Sb (named silicene, germanene, stanene, borophene, phosphorene, and antimonene, respectively). Xene single layers also serve as a background for the synthesis of new Xene-related materials (XRMs) such as Xene heterostructures and functionalized Xenes. Xenes can appear as metals, semimetals, semiconductors, and topological insulators thus allowing for a broad range of applications in nanotechnology. However only silicene has been integrated into transistors operating at room temperature albeit fast degradation. Nonetheless, a viable Xene-based nanotechnology is currently missing due to the lack of reliable standards for the Xene production and implementation. For this purpose, the proposal aims at developing viable schemes for high-quality crystal growth, environmental stabilization, and device integration of Xenes and XRMs frameworks. At first the effort will be focused on the high-quality synthesis of selected Xenes and XRMs by means of molecular beam epitaxy, and on their stabilization in encapsulated structures enabling subsequent processing into Xene-based device platforms. Validation of the Xene properties, quality, and performances will be carried out by means of advanced in situ and ex situ characterization of the atomic and electronic structure. Secondly, prototypical electronic device (e.g. field effect transistors or vertical diodes) incorporating stabilized Xene frameworks as active elements will be used to assess the Xene electrical behaviour and performances so as to establish a reliable Xene-based nanotechnology.