Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : GraViLa (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Université Eberhard Karl de Tübingen — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499438.0 €

Multimodal learning focuses on training models with data in more than one modality, such as videos capturing visual and audio information or documents containing image and text. Current approaches use such data to train large-scale deep learning models without human supervision by sampling pair-wise data e.g., an image-text pair from a website and train the network e.g. to identify matching vs. not matching pairs to learn better representations. We argue that multimodal learning can do more: by combining information from different sources, multimodal models capture cross-modal semantic entities, and as most multimodal documents are a collection of connected modalities and topics, multimodal models should allow us to capture the inherent high-level topology of such data. The goal of the following project is to learn semantic structures from multimodal data to capture long-range concepts and relations in multimodal data via multimodal and self-supervision learning without human annotation. We will represent this information in form of a graph, considering latent semantic concepts as nodes and their connectivity as edges. Based on this structure, we will extend current unimodal approaches to capture and process data from different modalities in a single structure. Finally, we will explore the challenges and opportunities of the proposed idea with respect to their impact on two main challenges in machine learning: data-efficient learning and fairness in label-free learning. By bridging the gap between those two parallel trends, multimodal supervision and graph-based representations, we combine their strengths of generating and processing topological data, which will not only allow to build new applications and tools but also opens new ways of processing and understanding large-scale data that are out-of-reach at the moment.

Mots-clés : multimodal learning,self supervised learning,graph structure learning
Projet ERC : POLYGEN (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Materials Engineering
Entité participante : École polytechnique Chalmers — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Polymers are the most ubiquitous materials class in modern society. Most of us spend a significant part of our lives in buildings made of polymers, using electronics manufactured with special polymers, and taking medicines encapsulated in carefully-optimized polymer formulations. In many cases, the design of new polymers is key to addressing global challenges in sustainability, health, and security. However, current polymer design methods rely heavily on trial-and-error experimentation and are grounded on imprecise assumptions about their structure, which is not only time-consuming and costly, but also offers limited insights into the relationship between structure, synthesis, and properties beyond the simplest polymers. While data-driven methods for small molecule engineering have flourished in recent years, artificial intelligence (AI) methods designed for polymers remain severely underdeveloped due to the unique challenges they pose. Their design involves navigating a noisy data space in search of compounds satisfying a complex set of specifications, made even more challenging when we’re interested in sampling novel, unique materials. The lack of well-defined, regular structures in polymers, coupled with a vastly different synthesis space, has also led to inefficient representations for them and an overreliance on hand-crafted simplifications, necessitating the development of information-rich yet scalable representa-tions for diverse polymer classes. I will tackle these gaps in AI-guided polymer design through the de-velopment of new paradigms for (1) learnable polymer representations and (2) generative AI frameworks for polymer design and synthesis. The methods I will develop bridge unique ideas from polymer science and AI to create a ground-breaking new approach for engineering polymers to specification and transform the current polymer design landscape across many industries via scalable data-driven strategies.

Mots-clés : artificial intelligence,ai,deep generative models,generative ai,polymer design,polymer engineering,contrastive learning,hypergraphs,representation learning,deep learning,hierarchical representations
Projet ERC : PhiSci (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Texts and Concepts
Entité participante : Université de Gand — Pays : Belgique — Rôle : PI
Acronyme de l'entité : UGent
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1464300.0 €

Philology once defined what it meant to be scientific – and it may yet once again. Increasingly a broad array of scholars using digital methods cite the historical accomplishments of philology as a model for systematic study around unwieldy and heterogenous textual corpuses. Despite this renewed interest, there is still no systemic account for the huge range of activity and aegis, data and networks, that propelled philology to its status as a model or even the 'queen of science' in C19 Europe. In drawing on history of science, media studies, information studies and diverse textual methods, this project offers that holistic account of how and why philology as a 'science in the making' achieved such extraordinary success. It articulates the widely sought yet unachieved bridge that would permit rigorous interdisciplinary exchange between philology – its historical and contemporary iterations – and present-day endeavors in the fields of digital humanities, critical data studies, infrastructure studies and de/post-colonial studies. PhiSci takes philology seriously as a science and gives it the kind of treatment that has dominated history of science for the last generation. Pioneering a novel account of philology from the French Revolution to First World War, it pursues a central question: How did local ensembles of protocols, representation, instrumentation and cooperation consolidate into robust programs for the genesis of stable knowledge and knowledge communities? It gives special attention to heterogeneity and universality in key concepts and practices and to physical aspects like media and infrastructure: elements undervalued or rarely grasped in terms of their epistemic work for producing data, evidence and facts. PhiSci will thus explain how philology operated as a relational system that – in the diversity of its data and perpetual flux in its projects and personnel –projected unity that enabled it to wield a scientific authority greater than the sum of its parts.

Mots-clés : history of the humanities
Projet ERC : Ethics of Loneliness (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Texts and Concepts
Entité participante : Université de Gand — Pays : Belgique — Rôle : PI
Acronyme de l'entité : UGent
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1025860.0 €

Loneliness, which can be defined as the negative mental states (e.g. sadness, despair) that people experience when they feel that they do not have enough relationships, or not the right kinds of relationships, is a widespread phenomenon in many societies. Even prior to the current pandemic, surveys from Europe and North-America reported that 20 to 35 percent of adults between the ages of 65 and 79 say that they are frequently lonely, a figure that rises to 40 to 50 percent among those aged 80 and above. Other groups that are at high risk of chronic loneliness include informal care-givers; people with disabilities; and adolescents, with 40 percent of 16-24 year old Britons saying that they are 'often' or 'very often' lonely. These statistics are worrisome, as chronic loneliness has been found to contribute to e.g. depression; dementia; and poor physical health, with some medical experts arguing that its health effects can be compared to those of smoking 15 cigarettes a day. In addition to this, loneliness takes a heavy economic toll. In the UK, for instance, it is estimated that ill health associated with loneliness costs employers 2.5 billion GBP every year. Yet while the severity of these harms and economic costs makes loneliness prevention and alleviation a highly important topic to study (as psychologists and sociologists have long realized), philosophers have paid scant attention to it. The aim of this project is to fill this lacuna. Through ethical analysis and reflection on relevant philosophical, psychological and sociological literatures, it will investigate what moral duties private individuals and societies more broadly (i.e. states) have to help protect people from loneliness and to help ensure that people can protect themselves from loneliness. This will culminate in the development of the first normative theory of loneliness within the scholarly literature and in the establishment of a new sub-field within moral and political philosophy.

Mots-clés : loneliness,sociability social isolation lockdowns relationships
Projet ERC : FFHiggsTop (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental Constituents of Matter
Entité participante : Université de Bologne — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1104218.75 €

Modern experiments at the Large Hadron Collider will yield measurements of several important observables with percent-level accuracy. Similarly accurate theoretical predictions are however needed for comparison with data, unlocking its outstanding potential to test the Standard Model of fundamental interactions, its symmetry braking mechanism and its limitations, hence giving us important hints about new physics. High-precision predictions in high-energy physics are however affected by high complexity. This is currently preventing high-precision studies for crucial interactions, such as multi-particle interactions involving the top quark and the Higgs boson, due to the presence of a large number of external particles and massive external and internal states. At the core of these predictions is the perturbative calculation of scattering amplitudes, which need to be computed at least at next-to-next to leading order to match the experimental uncertainty. This task, for multi-leg massive processes, is beyond the capabilities of existing techniques and tools. FFHiggsTop aims to achieve a breakthrough in high-precision predictions for high-energy scattering processes involving many external particles and massive internal and external final states. This will be achieved thanks to the development of new revolutionary methods for scattering amplitudes, building on top of cutting-edge technology based on finite fields and functional reconstruction techniques. Its main objectives are: 1. Develop ground-breaking techniques for computing scattering amplitudes at higher orders in perturbation theory 2. Perform new high-precision phenomenological predictions for top - anti-top pair production in association with an electroweak vector boson or a Higgs boson. FFHiggsTop will unlock a range of new possibilities for studying fundamental interactions and develop new technology with a broad spectrum of possible applications in physics and other sciences.

Mots-clés : particle physics,scattering amplitudes,cross sections,loop integrals,finite fields,computer algebra
Projet ERC : FRIENEMIES (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Life Science
Panel : Environmental B iology, Ecology  and E volution
Entité participante : Université Eberhard Karl de Tübingen — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499190.0 €

Plants live in close association with microbes, establishing interactions that range from beneficial to detrimental. Molecular plant-microbe interactions have been extensively studied in flowering plants, whereas research in non-seed plant lineages has been hindered by the lack of pathosystems reflecting natural plant-microbe interactions. My team and I identified the first example of a fungus that establishes opposite interactions with flowering and non-seed plants. We discovered that the fungus Trichoderma, which promotes plant growth, defense, and stress tolerance in flowering plants, is pathogenic for other land plant lineages (non-vascular plants and ferns) indicating unexpected differences in land plant immunity and fungus behavior. The overarching goal of our project FRIENEMIES is to decipher the molecular evolution of plant-microbe interactions from pathogenic to beneficial across land plants. In FRIENEMIES, my team and I will characterize the molecular mechanisms involved in disease in non-seed plants using three genetically tractable non-seed model plants (two bryophytes and the C-fern) and a diversity panel of sequenced Trichoderma strains where we will identify fungal effectors, plant susceptibility and resistance genes and signaling pathways relevant for pathogenic interactions. Combining omics, reverse genetics, natural variation, and phylogenetic analyses we will reveal the conservation of susceptibility/resistance mechanisms in other plant lineages and determine the emergence of mutualistic interactions between Trichoderma and land plants. We envision that FRIENEMIES will describe fundamental differences in immunity across land plants, discover potentially novel and conserved molecular mechanisms mediating plant-microbe interactions, define their evolutionary trajectories, and reveal the key evolutionary switches associated with major innovations in land plants underlying the transition between susceptibility, resistance, and mutualism.

Mots-clés : evompmi,plant molecular evolution
Projet ERC : NEFELI (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and Communication Engineering
Entité participante : Université de Padoue — Pays : Italie — Rôle : Partner
Acronyme de l'entité : UniPd
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 42250.0 €

Efficient assessment of multiple bio/chemical and electrophysiology biomarkers directly at the area of interest is an indisputable asset in the individualized healthcare. However, current implantable systems and bioelectronic technologies still face limitations in ultra-sensitive bio-sensing, address the electrical and chemical aspects fragmentally, and depend on complex setups and computationally heavy off-line processing. Conventional von Neumann architectures face limitations in efficiently handling the increasing sensor output data that can be mainly attributed to the physical separation between sensing, memory, and computing units. Here the overall objective is to conceptualize a first-of-its-kind, miniaturized, and self-contained biosensing technology employing neuromorphic devices functioning as on-node sensors and processors (in-memory (bio)sensing and computing), in soft, flexible and bio-compatible materials and format. I will demonstrate this technology showcasing, in an in vivo animal model, a proof-of-concept implantable bio-interface that intelligently interrogates and classifies neurodegenerative disease-related bio/chemical and electrical biomarkers, coupled with active elements enabling precise adjustment of stimulation control parameters based on analog inputs. This technology holds great potential to advance our understanding and treatments of pathologies through multiplex electrical and chemical monitoring reducing the demands for power-intensive analog-to-digital conversion and computational processing. Furthermore, it paves the way for tailored interventions, laying the foundation for next-generation biomedical modulation systems.
Projet ERC : NEUROLIDAR (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and Communication Engineering
Entité participante : Université de Padoue — Pays : Italie — Rôle : PI
Acronyme de l'entité : UniPd
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1634825.0 €

The growing societal burden of brain diseases is one of the most significant health challenges globally. To help diagnose or treat these pathologies, there is widespread agreement that a deeper understanding is needed on the origin of neural disorders linked to electrical neural activity, neuromodulators dynamics and molecular alterations. To this aim, the combination of optical methods with genetically expressed molecular probes has enabled great advances in understanding brain mechanisms. However, the existing tools fail in capturing a comprehensive picture of multifaceted neural dynamics. In addition, the translational applications of these technique are hindered by the need for exogenous reporters. As a result, there is a major scientific and technological gap that obstructs the full exploitation of cutting edge neuro technologies to relieve the impact of brain diseases in the human population. To bridge this gap, this project proposes an innovative approach to monitor neural dynamics in the mammalian brain using label free light matter interactions without exogenous reporters. To do this, I will develop a radically novel strategy harnessing the temporal and spectral dynamics of light stimuli interacting with the brain through photonic neural interfaces enhanced by integrated optical nano modulators. This approach will surpass the limitations of existing techniques in monitoring the physical complexity of neural signals. I will aim at three main objectives: O1) developing label free molecular sensing in arbitrary deep brain regions O2) developing label free, multifunctional, ultrathin, all-optical probes for dissecting the local micro-circuitry O3) developing label free, large-scale all-optical systems for multifunctional recordings at the global brain scale.

Mots-clés : neurophotonics,optical neural implants
Projet ERC : MANAGE HSC (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
Entité participante : Université d'Ulm — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

In humans, aging of the hematopoietic system is associated with anaemia, impaired immune responses and increased frequency of myeloid malignancies. The hematopoietic system is maintained by hematopoietic stem cells (HSCs). HSCs reside in the bone marrow (BM) within specialized niches, where they self-renew and differentiate to produce all blood and immune cells. In the clinical setting of BM transplantation, HSCs from older donors result in a reduced transplantation success, with a worse level of engraftment and higher overall mortality. This demonstrates that there is a reduction in the function of aged human HSCs (hHSCs). Causes of this impaired hHSC function with age still remain unclear. Therefore, it is of great importance to uncover and subsequently target factors that contribute to hHSC aging in order to attenuate impaired haematopoiesis in the elderly. Due to the lack of meaningful human BM (hBM) niche models in the aging field, the current understanding of how extrinsic changes (stemming from the aged hBM niche) contribute to the aging of hHSC is far less advanced compared to the knowledge of the contribution of cell-intrinsic changes. Thus, this proposal focuses on providing a significant understanding of age-associated changes within the hBM niche and their contribution to the aging of hHSCs using an aging-centred hBM niche model. Transplantation of young BM hHSCs into this advanced niche model revealed that an aged hBM niche confers aging on young hHSCs. To this end, the target of the proposal is to address the following questions:  (i) How does aging affect the cellular and molecular composition of the hBM niche? (ii) To which extent does an aged hBM niche affect aging of hHSCs? (iii) What are the underlying mechanisms that confer an age-like function on hHSCs?  MANAGE HSC addresses both technological and conceptual challenges in studying the role of the hBM niche in regulating hHSC function in the context of normal hematopoiesis and aging.

Mots-clés : human bone marrow niche,human hematopoietic stem cell ageing,bone organ engineering
Projet ERC : Viral DNA (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems E ngineering
Entité participante : Université de Cambridge — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1641428.0 €

There are over 6,000 human genetic diseases, affecting hundreds of millions of people. The vast majority are caused by small mutations in DNA sequences which are potentially correctable, so the development of new gene editing tools will have major benefits for treating human genetic diseases. To tackle this challenge, we will harness the natural protein diversity of viruses to create new gene editing technologies.  We will study an unusual class of viruses that use a unique mechanism to replicate themselves, known as protein-primed DNA replication, which creates the rare feature of a protein-DNA covalent bond. These viruses are very understudied, yet they hold great potential for new biotechnology applications. We will study the protein-primed DNA replication machinery of these viruses, and then exploit this mechanism to develop a novel technology for synthetic biology – creating new ways to amplify and deliver DNA into living cells for gene editing.  Our research represents a major breakthrough on two accounts: Firstly, to discover and characterise large numbers of protein-primed DNA replication proteins from previously unstudied viruses; to establish a highly efficient, self-replicating system able to synthesise and amplify large amounts of protein-linked DNA product; and to study the molecular details of these viral replication proteins for the first time.  Secondly, to develop protein-linked DNA as a new platform technology for gene editing. Our strategy to engineer viral proteins will create protein-linked DNA molecules that are for the first time (i) actively nuclear-targeted and (ii) self-replicating, to address key limitations in the gene editing field and pioneer a new method for highly efficient homology-directed repair.  Overall, our proposal combines fundamental biological study and applied biotechnology research to transform our understanding of these viral proteins and engineer them for ground-breaking advances in gene editing and DNA delivery.

Mots-clés : synthetic biology,gene editing,protein engineering,molecular biology,virology,bioengineering