Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : DivCon (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Compl exity
Entité participante : Université de Göteborg — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499543.0 €

Human interaction is deceptively simple to engage in, yet surprisingly challenging to account for theoretically. Existing theories of language and cognition cannot fully account for the complex dynamics of verbal and non-verbal behaviours in interaction, which is becoming even more apparent with our increasing use of computer-mediated communication, such as the currently ubiquitous Zoom calls.  With DivCon, my vision is to transform our basic understanding of human interaction by showing how successful dialogue is driven by incremental, local and dynamic processes of mismatch management.In our everyday interactions, we continuously make predictions about what will happen next, based on how our own and others’ behaviour affects the world, to open up new possible courses of action. In dialogue, these predictions are about sounds, words, inferences and even non-speech actions such as gestures or eye gaze. If our expectations are not met, we have to ascertain if the mismatching input can be resolved, or integrated as a surprising but rewarding outcome (as in the case of humour).  DivCon will produce a suite of corpus and experimental data for exploring the timely issues of communication via different forms of computer-mediated communication, including text-based chats, video calls and virtual reality meetings. To do this, the project will create a novel experimental platform for experiments in real time live multimodal interactions using avatars and virtual reality. The formal arm of the project will develop a precise theory of divergence and convergence in interaction which unifies verbal and non-verbal dialogue phenomena including gesture, gaze, feedback and laughter, using core notions of prediction and underspecification. This model will be implementable in conversational AI –an important step in the path to genuinely adaptive conversational AI systems, which are still beyond the reach of researchers despite the promise of recent decades.

Mots-clés : dialogue interaction computer mediated communication cmc miscommunication
Projet ERC : INTRAPARTY (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : Université de Göteborg — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1496800.0 €

Political parties and voters form important relationships in a democracy. The conventional wisdom is that divided parties lose elections. Yet the empirical evidence for this is ad hoc, and there are good reasons to suspect that it is, at best, a conditional wisdom. Firstly, the factional groups that divide parties vary in many different ways, even if the conventional wisdom treats them all the same. Secondly, since factions have somewhat different preferences than the rest of the party, they could also be useful in representing additional segments of society. However, there is currently no systematic analysis of the impact of factions – whether negative or positive – on a party’s electoral result. INTRAPARTY is a comparative study of factions and their effects on political parties’ electoral success in Europe. By answering the overall research question of When and how can factions have positive effects on political parties’ electoral performance?, INTRAPARTY launches a new scientific inquiry that challenges the conventional wisdom and seeks to explain the positive effects of factions on parties’ electoral performance. It provides unprecedented theoretical and empirical insights into the true role of factions in representative democracies. The project elaborates an original theory explaining factional effects on parties’ electoral performance that accounts for the inherent balancing factions face between inducing pressure but not harm on their party. Factions constitute a source of representation and reputation to voters that was previously neglected. Empirically, the project breaks new ground by combining theory-testing and exploratory approaches from research in party politics, interest group, and computational social sciences. By constructing an original comparative dataset on factions and parties over time and designing creative survey experiments to test voters’ reactions, the project tests the effects of factions on parties’ electoral success in Europe.

Mots-clés : political parties factions elections public opinion exploratory and theory testing methods europe
Projet ERC : DeepNeuronEmbeddings (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Université de Göttingen — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : GAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Understanding the relationship between structure and function of cortical neurons and circuits is one of the key challenges in neuroscience. For inhibitory neurons, roughly 15 subtypes are well characterized and we know a fair bit about their function. However, the vast majority of neocortical neurons are excitatory. Yet we know little about how differences in the morphology of excitatory neurons relate to their computational properties in vivo. I hypothesize that there is a close correspondence between morphology and function of excitatory neurons: distinct subtypes can be identified not only by their morphological features, but also by how they respond to stimulation with natural stimuli. To test this hypothesis, I will build upon recent advances in machine learning and develop a data-driven approach to derive a "bar code" for each neuron: a low-dimensional representation of its morphological features and its response properties to natural stimuli. Using these techniques, I will tackle the structure-function question by harnessing a large-scale functional anatomy dataset: a combination of electron-microscopy reconstructions at sub-micrometer resolution with two-photon functional imaging of nearly all excitatory neurons in one cubic millimeter of the mouse visual cortex. If successful, my project could fundamentally change our view on the diversity of excitatory cell types and reveal how morphological features are linked to a neuron's computational output. It could pave the way towards a unified definition of cell types, one of the fundamental building blocks of the brain. The same approach could be used in other brain areas and even other cellular systems beyond the brain. More broadly, while machine learning is promising to transform the scientific discovery process as a whole, my project could serve as a prime example of this transformation process in neuroscience and show how machine learning can help to discover structure in nature.
Projet ERC : ChACE (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Soci al World and Its Interactions
Entité participante : Université de Durham — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499640.0 €

Cultural evolutionary theory is quickly gaining traction as a unifying framework for the social sciences. However, research into culture acquisition paints a picture of children as vessels for inheriting evolved culture from adults, rather than cultural producers in their own right. ChACE aims to overturn this long-established orthodoxy by taking child and adolescent peer cultures as a focal point. Neglected by cultural evolutionary theorists, peer cultures are incredibly well conserved across generations despite threats to social transmission inexistent in adult cultures, challenging our understanding of how cultures are maintained and changed across generations. By intensively studying children’s peer cultures, ChACE thus stands to discover novel causal mechanisms for cultural evolution. ChACE specifically advances two ground-breaking hypotheses built upon firm theoretical and empirical foundations in folklore, cultural evolution, human life history, and cognitive development: (1) Peer cultures evidence distinct cultural evolutionary mechanisms from adult cultures; (2) Knowledge produced as part of peer cultures helps communities adapt to social and ecological change. ChACE will develop a robust and transferable methodology to jumpstart the study of peer cultures. Reflecting best-practice workflows for causal analysis, ChACE will first build ethnographically informed agent-based models to derive testable and falsifiable predictions. These will be empirically evaluated via experiments, observations, surveys, and interviews with children and adolescents aged 4-16 years, and their caregivers, at four globally representative field sites undergoing rapid culture change: the Likouala (Republic of the Congo), the Omo Valley (Ethiopia), Toledo (Belize), and County Durham (U.K.). In doing so, ChACE will initiate a disciplinary course correction by driving forward an explicit and holistic account of children’s adaptive contributions to culture change.

Mots-clés : cultural evolution peer cultures child development cross cultural research culture change ethnography innovation social learning
Projet ERC : MYCO-SoilC (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Life Science
Panel : Environmental B iology, Ecology  and E volution
Entité participante : Université de Bayreuth — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499930.0 €

The world's soils are the largest terrestrial reservoir of organic carbon (C). Feedbacks between soil organic C and atmospheric CO2 will determine the future trajectory of climate change. However, predictions are largely uncertain because we still lack fundamental knowledge of the complex interplay between plants and microorganisms and its influence on C turnover. Most terrestrial plants live in symbiosis with mycorrhizal fungi. Previous work suggests that on a global scale soil C stocks are linked to the distribution of arbuscular mycorrhizal (AM) or ectomycorrhizal (ECM) plants. To date, it is not clear whether there is a causal relationship between mycorrhizal type and soil C storage. Answering this key question requires novel concepts that consider the mechanistic link between short-term C fluxes from plants to mycorrhizal fungi and C storage as an emerging ecosystem property. MYCO-SoilC will yield a comparative, systematic understanding of the dynamics of C input by mycorrhizal fungi to soil, their effects on C turnover and their implications for C storage in temperate forests dominated by AM or ECM trees. Achieving this ambitious goal, which involves a multitude of processes on different spatio-temporal scales, requires the development of ground-breaking technological innovations. Key innovations of MYCO-SoilC are 1) real-time visualization of 11C allocation in plant-soil systems, 2) construction of the first moving greenhouse for 13CO2-labeling of mini-forests, 3) coupling of quantum dot nanotechnology with isotope labeling to visualize organic nutrient uptake by fungi, and 4) combining isotope analysis with biomarker approaches to quantify the fungal necromass contribution to soil C. The MYCO-SoilC approach bears significant conceptual and technical risks which are mitigated by a response plan with alternative routes. MYCO-SoilC will create substantial knowledge on mycorrhizal mediated C turnover and facilitate predictions of soil-climate feedbacks.

Mots-clés : soil organic matter mycorrhiza isotopes biogeochemistry soil science
Projet ERC : SOFIA (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth System Science
Entité participante : Université de Göteborg — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2479552.0 €

Climate mitigation relies on accurate knowledge of Earth’s energy distribution, which informs us about the extent, rate, and locations of climate warming. Historically, the ocean has played a dominant role, absorbing 93% of the excess heat generated by human activities through the air-sea interface. Ocean heat content trends obtained from sparse observations show the Southern Ocean dominates this heat uptake. Climate models confirm this, indicating the Southern Ocean accounts for 75–83% of the global ocean heat uptake. However, inter-model comparisons show discrepancies in ocean heat uptake by 40% that arise from misrepresentation of high-latitude winds and poor parametrization of vertical heat transport induced by fine-scale (1–100 km) ocean circulation. The goal of SOFIA is to understand and quantify the dominant processes that are responsible for moving heat between the atmosphere and ocean interior, thereby advancing climate understanding and predictability. I hypothesize that the ocean-atmosphere heat exchange across the Southern Ocean is set by immense and turbulent storms, while the vertical transport of heat between the surface ocean and interior is strongly regulated by fine-scale ocean fronts, eddies and filaments located in regions of high eddy kinetic energy. I will investigate the role of these processes in heat exchange dynamics by integrating a state-of-the-art coupled ocean-atmosphere model simulation with novel in-situ data from the remote Southern Ocean using innovative autonomous ocean observing technologies. Using high-resolution ocean altimetry data from the newly launched Surface Water Ocean Topography (SWOT) satellite mission, I will reconstruct circumpolar maps of vertical ocean transport, advancing our global understanding and predictability of ocean heat uptake in response to climate variability and change.
Projet ERC : PolyMetal (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Université de Bayreuth — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1493812.0 €

Semiconducting polymers have enabled the development of highly successful technologies such as flexible solar cells and OLED displays. Advances in synthetic chemistry allow precise tailoring of the electronic properties of soluble and processable materials. In the coming decades, a transition to chiral versions of these materials could not only enable the emergence of new technologies, including spintronics and quantum computing, but also increase the energy efficiency of OLED displays by 100%. Existing strategies for developing chiral semiconducting polymers fall short in achieving the necessary performance levels for light polarization, especially when high energy conversion efficiency is required. This highlights the need for a new paradigm in macromolecular design.  PolyMetal focuses on the fundamental aspects of chemical synthesis to produce an unseen class of processable chiral semiconductors. Specifically, PolyMetal will develop an innovative class of semiconducting polymers based on one-dimensional chains of metal atoms held in place by helically chiral polymers. To achieve the precise enchainment of metal atoms, we will develop a selective approach to synthesize phosphorus(III)-based polymers, which are highly sought-after for applications in energy conversion and nanomedicine. Selective catalysis will be employed to determine the chirality at the phosphorus(III) centers, introducing a new concept for the control of polymer tacticity and hence material properties. Upon metal coordination and enchainment, all centers of chiral information will actively participate in optical transitions and lead to helically arranged exciton coupled chromophores at a macromolecular scale thereby maximize the circular polarization of emitted light which we will assess in chiral OLEDs. Simultaneously, the use of metals will achieve quantitative energy efficiency thereby unifying high quantum yields with high chiroptical activity, an immense challenge that needs to be solved

Mots-clés : main group chemistry
Projet ERC : SAMPDE (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : Université de Gênes — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1153125.0 €

This project will develop a mathematical theory of sample complexity, i.e. of finite measurements, for inverse problems in partial differential equations (PDE). Inverse problems are ubiquitous in science and engineering, and appear when a quantity has to be reconstructed from indirect measurements. Whenever physics plays a crucial role in the description of an inverse problem, the mathematical model is based on a PDE. Many imaging modalities belong to this category, including ultrasonography, electrical impedance tomography and photoacoustic tomography. Many different PDE appear, depending on the physical domain. Currently, there is a substantial gap between theory and practice: all theoretical results require infinitely many measurements, while in all applied studies and practical implementations, only a finite number of measurements are taken. We argue that this gap is crucial, since the number of measurements is usually not very large, and has important consequences, regarding the choice of measurements, the priors on the unknown and the reconstruction algorithms. Many safe and effective modalities have had very limited use due to low reconstruction quality. Within a multidisciplinary approach, by combining methods from PDE theory, numerical analysis, signal processing, compressed sensing and machine learning, we will bridge this gap by developing a theory of sample complexity for inverse problems in PDE. This will allow for the deriving of a new mathematical theory of inverse problems for PDE under realistic assumptions, which will impact the implementation of many modalities, guiding the choice of priors and measurements. Consequently, emerging imaging modalities will become closer to actual usage. As a by-product, we will also derive new compressed sensing results which are valid for a general class of problems, including nonlinear and ill-posed, and sparsity constraints. Collaborations with experts in the relevant fields will ensure the project’s success.

Mots-clés : inverse problems,compressed sensing,machine learning
Projet ERC : CIRCULARIZE (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : Université de Gênes — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Recent advances driven by the society’s need of implementing a more sustainable polymer chemistry, led to the development of several bio-based materials and the booming of the interest of using biocatalysts for polymer processing and recycling. The aim of CIRCULARIZE is to develop appropriate chemo-enzymatic methods to process bio-based resources to obtain advanced functional materials via the exploitation of highly selective reactions following the green chemistry principles. Further, the aim is, based on that knowledge, to improve the reaction efficiency of the biocatalysts and understand and control the surface and bulk properties of the produced materials in pursuit of novel smart materials and reactive surfaces. Current processes for the utilization of bio-based monomers and resources are unspecific and lead to products in poor quality due to side reactions. Specific biocatalysts could achieve optimal selectivity together with minimal damage to the monomers and polymers and provide high-quality advanced materials. When considering the multifunctional monomers deriving from biomass, the chemo catalytic techniques used by industry have some major shortcomings such as limited selectivity (leading to side reactions) and use of toxic catalysts and solvents. Using this knowledge, the CIRCULARIZE project will develop and apply techniques based on sustainable chemo-enzymatic processes, developing a series of fully bio-based hybrid polymers having smart characteristics (temperature-switchable, vitrimeric properties) while being able to finely tune their degradation via the insertion of enzyme-susceptible motifs. Methodologically, CIRCULARIZE strives to establish new approaches at the cross-sectorial biotechnology/polymer chemistry interface to extend the current knowledge on enzymes for polymer synthesis and processing and, in the long term, the development of a sustainable biopolymer chemistry able to build an endless selective carbon recovery loop.

Mots-clés : circular polymeric materials,functional polymers,surface functionalization,selective depolymerization,enzyme susceptible bonds
Projet ERC : MALIN (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and Communication Engineering
Entité participante : Université de Gênes — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1432734.0 €

This project will develop model-aware, i.e. physics-informed, learning methods for solving imaging inverse problems (IIPs) in fluorescence microscopy imaging (FMI). IIPs are frequently encountered in FMI whenever a visual representation of a biological sample needs to be reconstructed from incomplete and noisy input measurements. Such IIPs are typically ill-posed: their solution (if it exists) is unstable to perturbations. Classical model-based approaches reformulate the IIP at hand as an energy minimisation task. Such approaches rely both on the (approximate) knowledge of the complex physical processes involved and on the mathematical design of hand-crafted optimisation methods whose tuning is often very time-consuming. Concurrently, the impressive development of machine and deep learning methods has enabled the applied imaging community with new data-driven methodologies providing unprecedented results in tasks such as image classification. The performance of data-driven methods for solving IIPs in FMI, however, is halted by their intrinsic unstable behaviour. In MALIN, I propose an integrative paradigm where the stable performance of model-based approaches is combined with the effectiveness of data-driven techniques by means of shallow model-constrained learning and deep physics-informed generative approaches. The reliability of the model-aware methods proposed will be justified by theoretical results providing reconstruction and convergence guarantees. The study will further account for possible geometric invariances and imperfect physical modelling, showing robustness to modelling errors which are frequent when standard (low-cost) equipment is used. Algorithmic acceleration strategies and inexact/stochastic algorithms will be devised to guarantee efficient performance also under limited computational resources and training data. The methodologies will be deployed on several IIPs in FMI and democratised through the release of open software and plug-ins.

Mots-clés : inverse imaging problems,bilevel optimisation,hyper parameter selection,physics informed deep learning,non smooth optimisation,fluorescence microscopy,3d super resolution,light sheet microscopy,multi focal imaging