Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : DIACIDAL (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Life Science
Panel : Environmental B iology, Ecology  and E volution
Entité participante : Université d'Exeter — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 783188.25 €

Interactions between phytoplankton and bacteria play key roles in mediating carbon and nutrient fluxes in the oceans, yet the mechanistic basis and environmental impacts of such interactions are poorly understood. DIACIDAL addresses crucial unknowns in our understanding of the interactions of one of the most globally abundant phytoplankton, the diatoms, and their bacterial pathogens. This proposal builds on my team’s recent advances developing an innovative sampling pipeline to isolate environmentally relevant bacterial pathogens of diatoms. This work suggests that bacterial pathogenicity towards diatoms is more prevalent than recognised previously. Moreover, we have important new evidence that diatoms can sense bacterial pathogens to evade their attack. Our new framework will be coupled with cutting-edge molecular tools that I have pioneered to study diatom signalling, including biosensors to track real-time signalling responses in single diatom cells. These tools have already made important breakthroughs in our understanding of how diatoms sense their environment. Exploiting these advances, we will use culture-dependent (Tn-Seq, comparative ‘omics, and imaging) and new single-cell culture-independent approaches. By focussing on a model interaction, we will decipher mechanisms mediating bacterial pathogenicity towards diatoms, and elucidate how diatoms sense and evade their pathogens. This will reveal novel effectors and defence signalling systems. Finally, we will leverage the new tools and insights to quantify the prevalence of bacterial pathogenicity and diatom defence in natural ecosystems. This innovative, multidisciplinary program will advance understanding of mechanisms governing bloom dynamics, inform ecosystem models, and shed light on a poorly characterised environmental stressor confronting phytoplankton communities. This also has potential to unlock new translational opportunities for biotechnology e.g. novel antimicrobial and anti-fouling compounds.

Mots-clés : diatoms,algal blooms,pathogenic bacteria,microbial interactions,functional biology
Projet ERC : O-Health (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : Université Pompeu Fabra — Pays : Espagne — Rôle : PI
Acronyme de l'entité : UPF
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1998865.0 €

Non-communicable diseases (NCD) that involve load-bearing organs emerge silently according to complex mechanisms that are likely to involve inter-disease systemic communications. Clinical explorations cannot apprehend such intricate emergence, but I postulate that multiscale in silico models can. The digital twin for health has progressed a lot in the last decades, but multi-disease transversal modelling has not happened. It requires unique developments to couple small to large-scale model components with appropriate balance of phenomenological and mechanistic approaches, to cope with overwhelming biological complexity, preserve interpretability and incorporate real-world data. This is the niche of O-Health that proposes a scalable ecosystem of multiscale NCD models interrelated through a systemic model of low-grade inflammation. The project tackles such vertical and transversal physiology-based computational modelling through four major NCD, lung emphysema, atherosclerosis, intervertebral disc degeneration and knee osteoarthritis that affect load-bearing organs at different anatomical locations. The cellular /molecular scale components of each NCD model will vertically share predicted variables with an interface model of endothelial cell dysfunction that communicates with a transversal model of body-wide systemic communications. The O-health ecosystem will be modular and interoperable. Mechanistic modelling will be covered by finite element models at the organ /tissue scales and by agent-based (AB) models at the cell /molecular scales. AB models will incorporate high-level interaction graphs for interpretable phenomenological modelling where necessary. Graphs will merge knowledge projection and correlation models extracted from longitudinal population cohort data, also used to evaluate O-Health. Interoperability will be ensured through standard languages such as Field and Systems Biology Mark-up Languages, enabling the scalability of the O-Health ecosystem.

Mots-clés : computational biomedical engineering systems mechanobiology systems biology finite element modelling agent based modelling multi scale modelling digital twin for health physiology based computational modelling
Projet ERC : ClimateAdapt (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : Université Pompeu Fabra — Pays : Espagne — Rôle : PI
Acronyme de l'entité : UPF
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1565511.0 €

The fast pace at which the earth climate is changing is one of the major challenges of our time. Rising temperatures are expected to reduce agricultural productivity in developing countries, which face uncertain adaptation paths. The theoretical literature highlights two potential adjustment mechanisms: the reallocation of economic activity towards non-agricultural sectors or towards colder regions. In the first case, lower agricultural productivity generates a shift in comparative advantage towards non-agricultural sectors and a reallocation of workers from agriculture towards manufacturing and services. However, this path might not be feasible for regions with low manufacturing productivity. In this second case, the lack of local employment opportunities can lead to migration towards other areas. There is a rich theoretical and quantitative literature studying the relative importance of each margin of adjustment. However, these predictions are subject to a large level of uncertainty due to the lack of direct empirical evidence. In particular, we lack evidence on how labor and capital market frictions shape the adjustment to climate change. We are now in the position to produce this evidence. The last decade has been unusually hot, leading to many persistent extreme weather events in tropical countries, which triggered adaptation responses. In this project, we use newly digitized administrative reports on extreme weather events occurred in Brazil during the last two decades, a new meteorological measure of excess dryness relative to historical averages, and detailed social security and credit registry data to estimate the effects of climate change on i) the local economy of affected areas, ii) the magnitude of the labor and capital flows it generates and iii) factor allocation across sectors and firms in destination regions.

Mots-clés : climate change,agricultural productivity,structural change,migration
Projet ERC : Pers_and_Preferences (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Individuals, Markets and Organisations
Entité participante : Université Pompeu Fabra — Pays : Espagne — Rôle : PI
Acronyme de l'entité : UPF
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1694043.0 €

In recent years, the study of personality (noncognitive skills) has significantly increased in economics. There has been an explosion of empirical work that shows the promise and relevance of incorporating such factors into the field. But we lack a definition of relevant personality traits in terms of primitive preferences, the way we do with risk and time preferences, for instance. Most studies rely on self-reported surveys, but this is only part of the issue: without a sharp, agreed-upon definition, it is difficult to build models, make predictions or elicit these measures in a way that unambiguously separates the primitives from the outcomes. My aim is to provide these notions.  Part 1 focuses on attitudes towards success and failure, and the thresholds that separate one from the other. Such attitudes are not only present in the way we discuss personality (e.g. perseverance and tenacity), they also appear in seemingly distant strands of economics, notably through the widely used notion of gain-loss sensitivity, notions of aspirations, etc. Within a standard decision theoretic setting, we define such attitudes as features of the primitive preferences over lotteries, we characterize the corresponding utility representations, and provide indices of the intensity of each attitude, in a manner analogous to the Arrow-Pratt coefficients for risk-aversion. These notions have crucial implications on two main levels: first, they generate testable predictions for choice under risk, and novel insights about central notions within the established economics toolkit; second, they expand the domain of economics methodology to the study of personality, by providing choice-based measures grounded on preferences, thereby bridging the gap between insightful notions from psychology and the desiderata of rigorous economic analysis. These two aspects are the focus of Parts 2 and 3, respectively, which tackle the problem through a series of large-scale experiments.

Mots-clés : expected utility axiomatic decision theory loss aversion aspirations risk aversion reference dependence non cognitive skills cognitive skills bounded rationality choice theory preference identification
Projet ERC : StochMan (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : École polytechnique Chalmers — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1997651.0 €

Uncertainty is all around us and caused, for example, by the nature of a problem as in quantum mechanics, the lack of our precise knowledge as in porous media, or inaccuracies in measurements as in experiments with imperfect equipment. While traditionally and due to the lack of computing power, science and technology relied on deterministic models, recent developments allow to include randomness. This trend requires efficient simulation methods for models with uncertainty. In space-time problems such as moving biological cells and the surface of the ocean, the randomness could be modeled by a stochastic process given explicitly or described by stochastic PDEs. Fast and accurate methods for sampling the stochastic processes are the key when computing statistical quantities of the advanced models. The main contribution of the project is the development of a theoretical framework for evolving stochastic manifolds and their efficient simulation with analyzed algorithms. Special emphasis is paid to the situation when the evolving stochastic manifold is a moving surface disturbed by external forces and described by stochastic PDEs. The main steps of the project are divided into three objectives:  Obj. (A) From random fields on manifolds to stochastic manifolds.  Obj. (B) From stochastic processes to evolving stochastic manifolds.  Obj. (C) Solving PDEs on stochastic manifolds. The challenges are tackled based on recent advances in the simulation of Gaussian random fields on manifolds and their analysis obtained by the research team of the PI. This new breakthrough paves the way for the development of sampling methods for stochastic processes on manifolds and ultimately to evolving stochastic manifolds. To reach these goals, the PI's research group is complemented by specialists in geometric numerical integration, numerical methods for (stochastic) PDEs, and spatial statistics.

Mots-clés : random fields,riemannian manifolds,stochastic processes,efficient algorithms,regularity,convergence analysis,evolving surfaces
Projet ERC : QNattyNet (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and Communication Engineering
Entité participante : Université de Naples - Frédéric-II — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999531.0 €

The Quantum Internet (QI) – a global network interconnecting remote quantum devices – is envisioned as the final stage of the quantum revolution, unlocking unprecedent communication functionalities. However, the key engineering challenge yet to be solved is the design of a protocol suite for the QI that can fully leverage the marvels of quantum mechanics. Current state-of-the-art proposals for the QI protocol suite adopt a layering model, mimicking TCP/IP standard-de-facto protocol stack of classical Internet. But this design choice limits the embrace of quantum features to the message delivered by the network, by leaving fundamental network functionalities to operate classically. Conversely, here, I PROPOSE a different and novel design, aiming at embracing the quantum marvels, not only in the message but also in key network functionalities, enabling so a quantum-native functioning for the QI. The VISION is that this design will unlock ground-breaking communication opportunities, as new forms of network resilience. QNattyNet overall GOAL is to design a protocol suite capturing the complexity of the quantum functioning by implementing a major paradigm-shift: from hierarchical protocol relationships to dynamic relationships. This goal will be pursued through 3 scientific objectives: - To design innovative protocols and functionalities for the QI by exploiting the quantum functioning. - To design a protocol suite for the QI able to capture the complexity of the quantum functioning. - To develop the QNattyNet simulator for supporting both the research community and the industry in the design and evaluation of quantum networks. This project is expected to have a deep impact towards deployment of the QI outside the laboratories into real world, by providing (through the designed protocol suite) a hardware-agnostic high-level abstraction of the network functionalities, which is pivotal for the successful development of standards and certifications for the QI.

Mots-clés : quantum internet,quantum networks,protocol stack,quantum communications
Projet ERC : Elmo (Horizon Europe, appel 2021, Consolidator 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 : 2000000.0 €

How could a textile change its feel upon the push of a button? While we are accustomed to visual displays and loudspeakers, interactive tactile perception largely eludes our experience. Textiles that change their pliability and texture would allow for communication using our sense of touch. Potential applications abound, from human-machine interfaces for robotics to new forms of virtual reality.  To facilitate such a tuneable mechanical response, materials are needed whose stiffness can be altered. This project will use conjugated polymers to realise stimuli-responsive materials. The developed materials will be spun into fibres and yarns, which will be integrated into prototype textile devices that can undergo a reversible change in pliability and texture. The explored materials science concepts will open up a new line of research in the blossoming field of organic electronics, while the application-oriented part of the project opens new horizons for the interdisciplinary field of wearable electronics.

Mots-clés : conjugated polymers,conducting polymers,mechanical properties,stimuli responsive polymers,electrochemistry
Projet ERC : POLYVIR (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Materials Engineering
Entité participante : Université de Birmingham — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2458551.0 €

We are in a constant and ever evolving battle with viruses. Our only defence are vaccines or antivirals. Both are typically developed in response to viral outbreaks meaning that any defence we mount is reactive and not proactive. Despite the clear need for improved interventions against viruses, there is a lack of innovation, novel materials, targeted approaches and systematic studies. Building on my experience in extracellular antivirals, I have recently identified a series of star polymer materials that display low toxicity (both in vitro and in vivo), can be produced cheaply at scale, and are potent broad-spectrum virucides (in vitro and in vivo). This seminal finding represents a significant leap forward in the development of biocompatible virucides, and with investment in their development, they will be ready for future viral outbreaks. POLYVIR will push the boundaries and expand the synthetic space of these biocompatible virucides by implementing high-throughput, oxygen-tolerant polymerisation, combined with liquid-handling robots, to create libraries of polymer virucides. To screen this unprecedented number of antiviral samples, high- throughput, cell-free, virucidal assays and techniques will be developed and then implemented, enabling screening of a range of structure-property relationships and expanding the scope and breadth of these virucides. Expansion to include glycol-polymers will allow for a truly pan-viral biocompatible virucide to be produced. To prevent animal pandemics, we will also focus on improving their activity window by enabling activity in high-protein and/or low-temperature environments. Our potent virucidal polymers will be applied as coatings to engineer surface properties of a range of materials to create long-lasting broad-spectrum antiviral surfaces and textiles. This project will consolidate my research at the forefront of extracellular antivirals globally and produce the next generation of scientists in this globally relevant fie

Mots-clés : antiviral,virucidal
Projet ERC : ProtonsInProteins (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems E ngineering
Entité participante : Technion - Institut de technologie d'Israël — Pays : Israël — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2190000.0 €

Proton transfer (PT) reactions within proteins are fundamental in biological systems, such as within ATP production. To date, there are no direct means to measure specific PT pathways within proteins, and most research is based on following the end-product of the PT reaction across a natural proton pathway. Here, our goal is to develop a novel way to directly measure PT within proteins. Our new approach is based on placing proton donors and acceptors in specific places within proteins, in which the PT will be initiated only after light excitation of the system. To do so, we introduce here two noncanonical amino acids (ncAA) we developed, with a photoacid and a photobase as their residues that serve as the proton donor and acceptor, respectively. Our hypothesis is that the strong light-triggered driving force of PT in the excited-state (of ~11 pKa units) will initiate PT along the pathway from donor to acceptor. In the first objective, we will use our new ncAA with solid-phase peptide synthesis to design several peptide systems that will allow us to decipher the role of specific amino acids, the peptide structure, and the role of water in PT across the peptide using various ultrafast spectroscopy. In the second objective, we aim to design a mutually orthogonal system for the insertion of our two ncAA into a single protein. In our third objective, we plan to use our new experimental system for answering a unique set of questions in the field of biological PT that could not have been answered before, focusing on the systems of ATP synthase transmembrane complex and the soluble carbonic anhydrase enzyme. Our new approach is groundbreaking in the way we study and understand PT in biology and will enable researchers completely new capabilities resulting in fascinating new discoveries. Moreover, our new system can be translated into other fields in biology that require the local change in proton concentration within proteins.

Mots-clés : biophysics,proton transfer,genetic code expansion,ultrafast spectroscopy
Projet ERC : MALFOY (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Université technique de Berlin — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1962000.0 €

Despite a long series of research, computer attacks still pose a major threat to the security of digital systems. Different malicious actors, such as cybercriminals and intelligence agencies, continuously develop new offensive techniques to evade and outsmart existing defenses. As a result, security research is in a constant arms race and needs to anticipate novel developments as early as possible. However, one of the key technologies of the last years, machine learning, has received very little attention in offensive security so far. The simple question — ''how would a hacker use machine learning?'' — is largely unexplored and there is a striking gap in current research that hinders the anticipation of forthcoming threats. The project Malfoy closes this gap and systematically explores how machine learning can be applied for offensive computer security. By adopting the position of an adversary, we investigate how learning algorithms can be used to find security flaws, generate exploits, and construct computer attacks. To this end, we combine offensive security techniques with modern concepts for discriminative, generative, and reinforcement learning. Our goal is to assess how these techniques can interface with each other and improve their performance through learning. Based on this analysis, we become able to devise completely novel defenses that account for the presence of machine learning in the toolchain of attackers. Despite its offensive nature, the project thus strengthens computer security: First, it explores an uncharted area of research and hence will substantially expand our knowledge about modern computer attacks. Second, the project gives rise to novel and disruptive protection mechanisms, which enable us to move one step ahead of attack development. Finally, the project links two disconnected areas (offensive security and machine learning) and thereby establishes a new branch of joint research.

Mots-clés : vulnerability discovery exploit generation penetration testing generative learning,reinforcement learning