Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : DECOLLAGE (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Instituto de Telecomunicações — Pays : Portugal — Rôle : PI
Acronyme de l'entité : IT
Type d'entité : Recherche
Financement ERC de l'entité : 1790220.0 €

In recent years, transformer-based deep learning models such as BERT or GPT-3 have led to impressive results in many natural language processing (NLP) tasks, exhibiting transfer and few-shot learning capabilities.  However, despite faring well in benchmarks, current deep learning models for NLP often fail badly in the wild: they are bad at out-of-domain generalization, they do not exploit contextual information, they are poorly calibrated, and their memory is not traceable. These limitations stem from their monolithic architectures, which are good for perception, but unsuitable for tasks requiring higher-level cognition. In this project, I attack these fundamental problems by bringing together tools and ideas from machine learning, sparse modeling, information theory, and cognitive science, in an interdisciplinary approach. First, I will use uncertainty and quality estimates for utility-guided controlled generation, combining this control mechanism with the efficient encoding of contextual information and integration of multiple modalities. Second, I will develop sparse and structured memory models, together with attention descriptive representations towards conscious processing. Third, I will build mathematical models for sparse communication (reconciling discrete and continuous domains), supporting end-to-end differentiability and enabling a shared workspace where multiple modules and agents can communicate. I will apply the innovations above to highly challenging language generation tasks, including machine translation, open dialogue, and story generation. To reinforce interdisciplinarity and maximize technological impact, collaborations are planned with cognitive scientists and with a scale-up company in the crowd-sourcing translation industry.

Mots-clés : deep learning,natural language processing,attention mechanisms,computational models of higher level cognition
Projet ERC : LAMI-PERO (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Materials Engineering
Entité participante : Helmholtz-Gemeinschaft — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : HGF
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2349755.0 €

Photovoltaic technologies are cornerstones of all future scenarios for sustainable energy supply. Enhancing the efficiency and reducing the costs of photovoltaics is a challenge of utmost urgency and importance to the research field of materials engineering and science. To date, hybrid organic–inorganic lead halide perovskite semiconductors denote the most promising material class for future low-cost and high-efficiency next-generation photovoltaics. Major hurdles that hinder the economic breakthrough are the low stability and the large-area fabrication of high-quality perovskite thin films. In response, LAMI-PERO will break new ground by researching and developing a novel lamination process that is designed to enable the processing of unexplored perovskite semiconductors and heterostructures, more durable thin film morphologies, and novel high-efficiency device architectures. My team and I were among the first to report preliminary data on this lamination process. It combines two half-stacks in a high-pressure step and allows crystalizing the pre-deposited precursor materials into a high-quality perovskite thin film. Thereby, the lamination disentangles critical constraints of established sequential layer deposition. To reach its ambitious goals, LAMI-PERO will (1) close the knowledge gap about the underlying physics of the perovskite film formation during the lamination process using innovative in-situ characterization, (2) explore more stable thin film morphologies and novel perovskite semiconductors, (3) fabricate more durable and high-efficiency perovskite solar cells and perovskite-based tandem solar cells, and (4) demonstrate the scalability of the lamination process in view of future commercialization. To date, the lamination of perovskite thin films is largely unexplored and the proposed research implies high risks but bears the enormous potential of paving the way for a breakthrough regarding the longevity and scalability of perovskite photovoltaics.

Mots-clés : perovskite semiconductors,2d 3d perovskite heterostructures,thin film morphology,stability,longevity,large scale fabrication,lamination,solar cells,tandem solar cells,photovoltaics
Projet ERC : TRITIME (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Materials Engineering
Entité participante : Helmholtz-Gemeinschaft — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : HGF
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1994136.0 €

Hydrogen is an indispensable element in the energy transition and expected to be key for decarburization of the European society. Hydrogen embrittlement – recognized and in focus of materials science since almost 150 years – still causes catastrophic failure until today. It is well-understood that all mechanisms of hydrogen embrittlement materialize at the scale of individual defects, such as dislocations, grain- and phase-boundaries. But we are still missing a correlative measurement of the mechanical behaviour of individual defects and the local hydrogen content, which is urgently needed to assess the occurrence, importance and magnitude of mechanisms playing a role during hydrogen embrittlement. In aid of this, TRITIME for the first time facilitates the isolation, observation and quantification of hydrogen embrittlement mechanisms by TRITIum based microMEchanics. The mechanisms of hydrogen embrittlement will be isolated by small scale mechanical testing on samples containing only a few crystal defects. The defect properties are observed and measured by in situ micromechanical experiments in the scanning electron microscope and at synchrotron beamlines. Simultaneously, TRITIME will monitor the local hydrogen content by observing the decay of tritium with high spatial resolution, for which a unique tool will be developed. In addition, post mortem analysis using atom probe tomography and secondary ion mass spectroscopy take advantage of the reduced mobility of tritium. TRITIME will provide unprecedented insights into the local hydrogen content of newly formed slip bands, mobile and immobile dislocations and fracture surfaces. Consequently, if successful, TRITIME will obtain a mechanism-based, quantitative understanding of HEDE, HELP and their interplay. In doing so, TRITIME sets the base for a mechanism-based optimization of microstructures used in distribution and storage of hydrogen and, therefore, is an indispensable tool towards Europe`s hydrogen society.

Mots-clés : hydrogen embrittlement
Projet ERC : MESHOPTO (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Materials Engineering
Entité participante : Sorbonne Université — Pays : France — Rôle : Partner
Acronyme de l'entité : Sorbonne U
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 437562.5 €

Visual sense is vital for all of us. Blindness has severe negative psychological, social, and economical consequences, and degeneration of photoreceptors is a leading cause of it. Photovoltaic retina implants are the current electronic solution to restore vision loss due to photoreceptor degeneration. Since the state-of-the-art implants are based on photodiodes, which face challenges in terms of miniaturization, efficiency, and compatibility with mechanical and structural properties of the retina, artificial vision still falls short to overcome the legal blindness level. We propose a novel concept of Retinal Mesh Optoelectronics that will simultaneously satisfy (a) high-pixel density for high visual acuity, (b) conformability to match the natural curvature of the retina for optimal vision quality, (c) flexibility for coverage of a large area of the retina for a wide field of view, (d) seamless integration to keep the remaining healthy photoreceptors intact, (e) biocompatibility, (f) usage of safe capacitive current, (g) injectability and (h) removability. Toward this aim, we will initially develop efficient, thin, and cellular-sized photovoltaic neural interfaces based on quantum dots and nanowires. For that, non-toxic quantum dots that have strong light absorption at near-infrared will be synergized with the nanowires that have unique light-trapping and high surface area for efficient photostimulation of neurons. Then, we will translate these devices to porous and flexible tissue-like retinal implants for artificial vision. Starting from the nanomaterial synthesis to optoelectronic device fabrication and bioelectronic mesh formation, this challenging innovation combining nanomaterials, photonics and abiotic-biotic interfaces will be explored from primary neurons up to in-vivo experimental models of photoreceptor degeneration in order to move the results toward clinical application.

Mots-clés : quantum dot,nanowire,neural interface
Projet ERC : NK InSight (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
Entité participante : Trinity College — Pays : Irlande — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999681.0 €

Age-related macular degeneration (AMD) is the leading cause of irreversible blindness in the world. The number of people with AMD has reached 196 million, with ~1 in 10 people over 55 years already showing early signs of the condition. 70% of those who progress to late-stage disease will develop neovascular (nv)AMD due to uncontrolled growth of blood vessels behind the retina. In fact, aberrant growth of retinal or choroidal vasculature is a hallmark of diabetic retinopathy and nvAMD. Collectively, these are the most common causes of central vision loss globally. Identification of new biological processes that regulate neovascularization are urgently needed to open new therapeutic options for patients. This is a challenge; but with recent longitudinal studies showing unequivocally that current gold-standard care has a therapeutic ceiling, it presents a critical unmet clinical need. I have recently discovered that natural killer cells (NKs) are found in neovascular lesions in human AMD and our mouse model data suggests that these innate lymphoid cells (ILCs) play a role in inhibiting pathology. Using scRNAsequencing I discovered a defined subset of NKs in neovascular lesions in a model of resolving nvAMD. I now hypothesize that, in contrast to expectations, these ILCs play a critical role in remodelling neovascular lesions, limiting pathology as opposed to exacerbating it. NK cells are completely unstudied in the retina. Here, I propose that manipulating NK cells may represent a novel therapeutic strategy for the prevention and treatment of neovascular eye disease. Objectives: 1) Examine NK cell function in models of retinal neovascular disease 2) Profile NK cells in AMD pathology in a human study 3) Develop microfluidic “neovascular retina-on-a-chip” model to examine NK cell mechanism-of-action 4) Determine if NK cell therapy can ameliorate neovascular eye disease Goal: To harness the power of NK cell activity to treat neovascular eye diseases

Mots-clés : natural killer cell,retinal neovascular disease,age related macular degeneration amd
Projet ERC : NorseMap (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Studies of Cultures and Arts
Entité participante : University College Cork — Pays : Irlande — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999519.0 €

The modern understanding of the Vikings has developed over a thousand years to reflect the politics, identities and cultural memories of very different communities across Europe. This shared history impacts on many aspects of contemporary society and is appropriated in increasingly diverse ways. NorseMap posits that the oversignification of the Viking past can be approached as an ecology of interactions within which ideas of European culture and identity are made and remade. While this working hypothesis is based on observation of the current reception landscape, it is difficult to test because of a lack of data on public perceptions of the Vikings and the limitations of reception studies as currently conceived to map such a complex legacy. The guiding question that NorseMap asks – how is the Viking past used today, and how has public understanding of the Vikings evolved over time? – thus poses profound challenges for the discipline at both an applied and conceptual level. The applied challenge is how we capture widely dispersed data on uses of the Viking past in the present day. The conceptual challenge involves rethinking reception studies so it can address historical legacy at a macro level and account for the complexities of the reception ecosystem. NorseMap will make a step change in cultural heritage crowdsourcing, taking the model of app-based GIS mapping used for large-scale citizen science documentation projects and adapting it to the humanities. Reorienting the discipline to map the Viking legacy will entail drawing together various strands of reception history – Literature & Art; Branding & Tourism; Politics & Identity – and demonstrating how they have interacted to create a uniquely pliable cultural memory, using a conceptual lens inspired by Actor-Network Theory. The resulting Deep Map of reception will reveal the multiple influences that intersect in any individual reimagining of the Vikings and open up new ways of thinking about the remediated past.

Mots-clés : old norse viking studies reception cultural heritage crowdsourcing community engagement cultural legacies public history
Projet ERC : LASD (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Université de Technologie de Kaiserslautern — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : TUK
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1998956.0 €

Formal language theory is indisputably one of the most successful theories in theoretical computer science with many applications in such fields as formal verification, programming languages, and databases, to name a few. Despite this, the standard restriction to finite alphabets has been a limiting factor in the applicability of the theory in various important application domains, wherein sequences with data (i.e. ranging over an infinite domain like the set of integers and the set of strings) naturally arise. This has motivated an extensive study of automata over infinite alphabets in the last three decades, which aims to develop algorithms for analyzing data languages. It was, however, quickly realized that permitting anything but the simplest data reasoning results in undecidability for the most fundamental models in the theory. Given the growing practical needs for tools for reasoning about sequences with data, it is crucial that we break through this undecidability barrier. The aim of the project is to overcome the current undecidability barrier in extending automata theory over infinite alphabets with complex data reasoning. The first concrete objective is, thus, to identify new decidable logic/automata models for data languages that permit complex data reasoning over various data domains (e.g. integer linear arithmetic, real-closed fields, string constraints, etc.), and to develop efficient algorithms for analyzing them. In addition to making fundamental theoretical contributions in automata theory over infinite alphabets, I also aim to address the current practical needs for tools for reasoning about sequences. Thus, the second objective of the project is to transfer new algorithmic insights to the following important application domains: (1) querying graph databases, (2) verification of list-manipulating programs, and (3) interpretable machine learning for sequences.

Mots-clés : logic,automata,data words,decidable theory,verification
Projet ERC : Icevolt (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : Université norvégienne de sciences et de technologie — Pays : Norvège — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2000000.0 €

Water and aqueous solutions release a huge amount of thermal energy during freezing. Accessing energy utilizing the freezing process has never been attempted until today. It is known that orderly freezing of dilute aqueous solutions can result in electrical potentials as great as 230 volts at the ice-solution interface, namely the Workman-Reynolds freezing potential (WRFP), and a maximal electrical current of 1 µA. Based on our newly published results of counter ion separation during icing growth in ionic liquids, this project aims to combine high-throughput computing and nanotechnology for probing the approach of controlling and utilizing stable WRFPs and for the first time to enable energy exploration in the freezing process of aqueous solutions. Specifically, ionic liquids will be screened for maximizing WRFP and electrical energy output via atomistic modeling and multiscale experiments. Critical parameters for WRFP, including ion concentration, temperature and freezing rate, will be investigated for their effects in electrical energy generation. Novel electrical energy output based on WRFP will be constructed and tested, which will seed an entirely new green energy acquisition research and application field. The project, termed Icevoltaics, explores an un-touched energy source and provides highly innovative solutions of the future and clean energy.
Projet ERC : LEGACIES (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : Université norvégienne de sciences et de technologie — Pays : Norvège — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1977464.0 €

While today’s political map divides the globe into about 200 sovereign states, there were hundreds more in the 18th and 19th centuries. Although most of these “historical states” no longer appear on modern maps, they have left behind institutions, symbols, and elite networks. What are the legacies of these “historical states”? We propose a new theoretical framework to unpack the impacts of historical states on patterns of conflict and democracy in the modern world and a powerful new method capable of generating high-resolution data on the topographies of statehood, globally, between 1750-1920. Our method overcomes major limitations and obstacles in current practices of studying the legacies of historical states and enables us to transcend 2D Cartesian mapping assumptions that are poorly equipped to render statehood in what were often decentralized international systems. Drawing on our new theoretical framework, we use the high-resolution LEGACIES data to estimate how historical statehood has shaped contemporary patterns of dissent and democracy. Our project has the potential to transform the way international relations scholars see and understand the international system(s) of the 18th and 19th centuries and how these legacies have shaped the political contours of the modern world.

Mots-clés : historical states,mapping,dissent,democratization
Projet ERC : IndexThePlanet (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Life Science
Panel : Integrative B iology : from Genes and Genomes to S ystems
Entité participante : Institut Pasteur — Pays : France — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 1933625.0 €

Nowadays a huge amount of planetary DNA and RNA sequencing data is available and continues to double every two years. However, efficiently analyzing this data is impossible due to its size measured in petabases. Many high-impact biological discoveries could be made but are prevented by the lack of fast search algorithms. I recently demonstrated this potential by discovering an order of magnitude more RNA virus species within all public RNA samples. A global index, i.e. a planetary genomic search engine, would unlock instant and inexpensive search within petabase-scale data.  I hypothesize that I can create a searchable index for all of the public DNA and RNA sequencing data. Leveraging my unique expertise across algorithms and data structures for biological sequences, my plan is to design efficient methods to assemble and compress all available sequencing data, and then construct an external-memory index that will support versatile biological queries.  A planetary sequencing data index will enable a myriad of bioinformatics analyses that are currently out of reach. I will demonstrate the utility of the index by constructing a database of human transcripts with novel diseases associations, discovering novel microbial species, and providing a search engine for environmental metagenomes. The resulting unprecedented collection of assembled genomes and compressed reads will lift a major challenge in data accessibility, improving its efficiency by several orders of magnitude, revolutionizing the scale of future bioinformatics analyses.

Mots-clés : bioinformatics,sequencing data,algorithms,indexing,k mers,compression,genome assembly