Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : CASTLE (Horizon Europe, appel 2022, Synergy grants)
Entité participante : Université Northwestern — Pays : Etats-Unis — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1990689.0 €

Chirality is a key property of molecules important in many chemical and nearly all biological processes. Recent observations have shown that electron transport through chiral molecules attached to solid electrodes can induce high spin polarization even at room temperature. Electrons with their spin aligned parallel or antiparallel to the electron transfer displacement vector are preferentially transmitted depending on the chirality of the molecular system resulting in Chirality-Induced Spin Selectivity (CISS). The long-term vision of the CASTLE project is to transform the CISS effect into an enabling technology for quantum applications. This will be accomplished by achieving four key objectives. 1) The occurrence of CISS will be studied at the intramolecular level by photo-inducing rapid electron transfer within covalent donor-chiral spacer-acceptor molecules to generate long-lived radical pairs (RPs). 2) Direct detection of RP spin polarization will be performed using time-resolved and pulsed electron and nuclear magnetic resonance techniques. In addition, polarization transfer from one of the radicals comprising the spin-polarized RP to a stable molecular spin (Q) will be used to initialize the quantum state of Q, making it a good qubit for quantum applications, particularly sensing. 3) Quantum mechanical studies of the CISS effect will provide predictive models for molecular qubit design. 4) The CISS effect will be used to control, readout, and transfer information in prototypical devices embedding hybrid interfaces based on semiconducting or conducting substrates, thus dramatically advancing the use of molecular spins in quantum information technologies targeting high-temperature operation. These devices will be used also to prove molecule-based Quantum Error Correction. The knowledge acquired with CASTLE will impact a wide range of fields, including magnetless spintronics, dynamic nuclear polarization for NMR signal enhancement, catalysis, and light harvesting.

Mots-clés : chirality,electron transfer,molecular magnetism,spin selectivity,spintronics,supramolecular chemistry,coordination chemistry
Projet ERC : DarkQuantum (Horizon Europe, appel 2023, Synergy grants)
Entité participante : DESY — Pays : Allemagne — Rôle : Partner
Type d'entité : Recherche
Financement ERC de l'entité : 1636250.0 €

The unresolved nature of the Dark Matter permeating our Universe is one of the most pressing questions of modern science. It is connected to our very understanding of reality at the most fundamental level. The axion DM paradigm has recently emerged as one of the most compelling hypothesis to solve this question: Dark Matter would be composed by very light and very feebly interacting axions. This paradigm is strongly motivated by theory, and predicts a clear signal in terrestrial experiments called axion haloscopes. Pioneering experiments have reached enough sensitivity to test some realistic axion models in limited mass ranges, so far without a positive signal. However, there is still a large viable axion parameter space to be explored. The methods used to date will be inefficient to perform such a challenging task. Here our DarkQuantum consortium proposes a new way of addressing this gap using quantum sensing technologies and hybrid quantum systems. Specifically, we will combine quantum technologies and well established particle physics environments at CERN or DESY devoted to the detection of axions in the galactic halo. Building quantum-enhanced setups in particle physics environments is extremely challenging and needs expertise from very different fields of physics. Our consortium brings together experts from quantum circuits, very-low temperature cryogenics, quantum measurements and particle physics, to build two quantum-enhanced haloscopes with unprecedented sensitivity and mass scanning range. The novel sensing strategies of the DarkQuantum project could lead to the experimental detection of axions for the first time. Such a fundamental discovery in connection with the long-standing DM problem would lead to a breakthrough in Physics

Mots-clés : axions dark matter axion haloscope axion like particle photon counter qubit transmon high temperature superconductor low background techniques underground physics machine learning
Projet ERC : OCEAN (Horizon Europe, appel 2022, Synergy grants)
Entité participante : University of California System — Pays : Etats-Unis — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 3958426.25 €

Until recently, most of the major advances in machine learning and decision making have focused on a centralized paradigm in which data are aggregated at a central location to train models and/or decide on actions. This paradigm faces serious flaws in many real-world cases. In particular, centralized learning risks exposing user privacy, makes inefficient use of communication resources, creates data processing bottlenecks, and may lead to concentration of economic and political power. It thus appears most timely to develop the theory and practice of a new form of machine learning that targets heterogeneous, massively decentralized networks, involving self-interested agents who expect to receive value (or rewards, incentive) for their participation in data exchanges.  OCEAN will develop statistical and algorithmic foundations for systems involving multiple incentive-driven learning and decision-making agents, including uncertainty quantification at the agent's level. OCEAN will study the interaction of learning with market constraints (scarcity, fairness), connecting adaptive microeconomics and market-aware machine learning.  OCEAN builds on a decade of joint advances in stochastic optimization, probabilistic machine learning, statistical inference, Bayesian assessment of uncertainty, computation, game theory, and information science, with PIs having complementary and internationally recognized skills in these domains. OCEAN will shed new light on the value and handling of data in a competitive, potentially antagonistic multi-agent environment, and develop new theories and methods to address these pressing challenges. OCEAN requires a fundamental departure from standard approaches and leads to major scientific interdisciplinary endeavors that will transform statistical learning in the long term while opening up exciting and novel areas of research.

Mots-clés : machine learning,federated learning,computational game theory,multi armed bandits,bayesian inference,markov chain monte carlo,multi agent reinforcement learning,online matching markets,data market,incentive
Projet ERC : Custom-Made (Horizon Europe, appel 2024, Synergy grants)
Entité participante : Université de Milan — Pays : Italie — Rôle : CO-PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2500000.0 €

The rising incidence of age-associated neurodegenerative diseases, due to increased life expectancy, poses a challenge to the brain's limited regenerative capabilities. Stem cell-based therapies offer promise for addressing crucial neuron loss, particularly in Parkinson’s and Huntington’s diseases (PD and HD). Ongoing first-generation stem cell trials, replacing damaged neurons with healthy ones, represent a notable advancement. However, their impact is constrained by a ‘one-cell-fits-all’ generic therapy that does not take in account the diverse clinical needs due to variations in disease etiology, stage, patient heterogeneity, and daily fluctuations in graft requirements. CUSTOM-MADE is a transformative initiative advancing knowledge and crafting a tailored portfolio of stem cell products for individual patients or patient subgroups, notably targeting PD and HD. Integrating stem cell technologies, genomics and bioengineering, CUSTOM-MADE develops rational graft composites of therapeutic neurons and complementary cells to meet the clinical needs of individual patients. Moving into preclinical animal models, the project refines graft connectivity and functional recovery post-transplantation. A groundbreaking leap involves modeling circuitry reconstruction in a fully humanized 3D in-vitro bioengineered system - the connectoids, allowing detailed studies at cellular and molecular levels for defining factors controlling correct and functional graft integration. For increased translatability, CUSTOM-MADE pioneers the generation of cell products whose activity can be self-regulated or tuned to the patients’ individual requirements. The project implements strategic measures to shield grafts from pathology and introduces the innovative concept of grafted cells exhibiting disease-modifying activity. In essence, CUSTOM-MADE drives the field toward customized stem-cell therapies for PD and HD, marking a paradigm shift in the treatment landscape of neurodegenerative diseases.

Mots-clés : connectoids,biosensors,chemogenetics,activity regulators,parkinson s disease,huntington s disease
Projet ERC : NEMESIS (Horizon Europe, appel 2022, Synergy grants)
Entité participante : Fondazione per la ricerca biomedica avanzata ets — Pays : Italie — Rôle : Partner
Acronyme de l'entité : VIMM
Type d'entité : Recherche
Financement ERC de l'entité : 634916.25 €

Focal brain disorders, including stroke, trauma, and epilepsy, are the main causes of disability and loss of productivity in the world, and carry a cumulative cost in Europe of about 500 billion euro/year. Now, physicians diagnose and treat these conditions as if they were caused by local dysfunction due to the pathological process. However, there is growing evidence that, in most neurological and psychiatric disorders, clinical symptoms reflect widespread network abnormalities. Normalization of such network abnormalities through “circuit-based” stimulation would therefore improve function. However, this form of therapy is currently limited by numerous factors: lack of knowledge about the underlying mechanisms and their behavioural relevance; inability to map these abnormalities onto single patients; and, most importantly, a principled understanding of where and how to stimulate the brain to produce functional recovery. ‘NEMESIS’, from Ancient Greek as “give what is due”, aims to “give an injured brain what is missing”, i.e., restore through stimulation normal activity in dysfunctional brain circuitries. By synergizing people, concepts, and technologies, NEMESIS will first characterize the effects of focal injury at multiple spatial and temporal scales (from whole brain to local circuits). Through the combination of observational (e.g., fMRI, EEG, calcium imaging, LFPs) and causal methods (e.g., electro-magnetic stimulation, optogenetics) NEMESIS will test the hypothesis that disconnected networks lie in a “sleep-like state” that impairs communication. Thirdly, NEMESIS will create whole brain models of structure/function to predict the effect of individual lesions and simulate novel stimulation protocols aimed at “re-awakening” the disconnected brain. Finally, proof-of-concept interventions that combine circuit-based stimulation and behavioural training, guided by modelling and animal studies, will be tested to restore normal activity, and so give back what is due.

Mots-clés : focal injury,brain networks,brain rhythms,sleep,slow wave oscillations,whole brain computational models,brain stimulation,brain connectivity,fmri,eeg,lfp,calcium imaging,tms,7t mri
Projet ERC : MEDGREENREV (Horizon Europe, appel 2022, Synergy grants)
Entité participante : Université de Bâle — Pays : Suisse — Rôle : Partner
Acronyme de l'entité : UniBasel
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 224386.25 €

As Mediterranean societies prepare for the impacts of climate change over the next few decades, a case study to inform their future resilience is associated with one of the defining events in world history: the emergence of Islam in the 7th century, the subsequent Arab (and later Berber) conquests of the southern and western Mediterranean, their associated population movements and the environmental adaptations that enabled these new societies to flourish. The introduction of new forms of agriculture that transformed the economies of the conquered regions has been previously framed as the “Green Revolution”. Moving beyond the limited focus of earlier research, with its restricted spatial and chronological scope, this project will adopt an integrated ecological approach, encompassing plants, animals and soils, from production through to consumption, and compare the impact and legacy of environmental transformations associated with long-term societal change in the western Mediterranean (Iberia and Morocco), beyond the initial Arab/Berber conquests. Adopting a broader chronological span, from the century before the conquests through to the century after the dissolution of the last Islamic polity in Iberia, the transition from Islamic to Christian regimes in Iberia will be compared, for the first time, with the persistence of Islamic polities in northwest Africa, against the backdrop of climatic fluctuations. This will enable relationships between environmental transformations and sequences of political and demographic change to be effectively contextualised. The synergy between our groups will combine regional specialisms with a wealth of scientific expertise, enabling us to conduct the first long-term and inter-regional study of how conquest, migration and encounters with resident populations and landscapes drove environmental transformation in the western Mediterranean during this formative period in world history.

Mots-clés : geoarchaeology,zooarchaeology,archaeobotany,palynology,gis,multiculturalism,state formation,mediterranean,iberia,north africa,longue dur e,arab conquest,medieval green revolution
Projet ERC : AI4AMR (Horizon Europe, appel 2024, Synergy grants)
Entité participante : Helmholtz-Gemeinschaft — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : HGF
Type d'entité : Recherche
Financement ERC de l'entité : 2030428.0 €

Antimicrobial resistance (AMR) is one of the most pressing global health problems of our times. To counteract AMR, we urgently need new antibiotics, particularly with novel modes of action (MoA). However, while typical antibiotic screening pipelines can identify compounds that impair bacterial growth, they are unable to predict drug targets and MoA so must be followed up by time-consuming target identification steps. By synergizing our expertise in microbiology, genetics, advanced microscopy, metabolomics, medicinal chemistry, computational biology and artificial intelligence (AI), we propose to create a new pipeline at the forefront of the antibiotic discovery field that will be capable of informing simultaneously on the bioactivity and MoA of new antibiotic candidates. Working with seven pathogens, our improved acquisition strategies for both imaging-based high-content screening and metabolomics will generate a massive dataset of rich multidimensional phenotypes of libraries of genetic mutants and of bacteria exposed to a range of perturbants, at unprecedented scale. Deep learning analyses will then enable us to explore these massive datasets to correlate chemical-induced phenotypes to those from mutants, linking drugs to genes to elucidate the target/MoA of new drugs. This innovative pipeline will enable us to explore unique chemical spaces, including complex natural product extracts (without the need for isolation of individual components) and novel synthetic compounds. Promising candidates with novel MoA will be tested against drug-resistant clinical isolates and against a future pandemic 'pathogen X', demonstrating our pipeline as an AI-powered solution for achieving higher productivity in antibiotic discovery. AI4AMR will provide the community with a new pipeline to efficiently screen large compound libraries to identify novel antibiotics and define their MoA and target, helping directly to combat AMR.

Mots-clés : antibiotics,metabolomics,chemical libraries,natural products,mutant libraries,high content imaging,artificial intelligence,drug discovery
Projet ERC : NEMESIS (Horizon Europe, appel 2022, Synergy grants)
Entité participante : Université de Milan — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1400591.25 €

Focal brain disorders, including stroke, trauma, and epilepsy, are the main causes of disability and loss of productivity in the world, and carry a cumulative cost in Europe of about 500 billion euro/year. Now, physicians diagnose and treat these conditions as if they were caused by local dysfunction due to the pathological process. However, there is growing evidence that, in most neurological and psychiatric disorders, clinical symptoms reflect widespread network abnormalities. Normalization of such network abnormalities through “circuit-based” stimulation would therefore improve function. However, this form of therapy is currently limited by numerous factors: lack of knowledge about the underlying mechanisms and their behavioural relevance; inability to map these abnormalities onto single patients; and, most importantly, a principled understanding of where and how to stimulate the brain to produce functional recovery. ‘NEMESIS’, from Ancient Greek as “give what is due”, aims to “give an injured brain what is missing”, i.e., restore through stimulation normal activity in dysfunctional brain circuitries. By synergizing people, concepts, and technologies, NEMESIS will first characterize the effects of focal injury at multiple spatial and temporal scales (from whole brain to local circuits). Through the combination of observational (e.g., fMRI, EEG, calcium imaging, LFPs) and causal methods (e.g., electro-magnetic stimulation, optogenetics) NEMESIS will test the hypothesis that disconnected networks lie in a “sleep-like state” that impairs communication. Thirdly, NEMESIS will create whole brain models of structure/function to predict the effect of individual lesions and simulate novel stimulation protocols aimed at “re-awakening” the disconnected brain. Finally, proof-of-concept interventions that combine circuit-based stimulation and behavioural training, guided by modelling and animal studies, will be tested to restore normal activity, and so give back what is due.

Mots-clés : focal injury,brain networks,brain rhythms,sleep,slow wave oscillations,whole brain computational models,brain stimulation,brain connectivity,fmri,eeg,lfp,calcium imaging,tms,7t mri
Projet ERC : OCEAN (Horizon Europe, appel 2022, Synergy grants)
Entité participante : École pour l'informatique et les techniques avancées — Pays : France — Rôle : CO-PI
Acronyme de l'entité : EPITA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 738320.0 €

Until recently, most of the major advances in machine learning and decision making have focused on a centralized paradigm in which data are aggregated at a central location to train models and/or decide on actions. This paradigm faces serious flaws in many real-world cases. In particular, centralized learning risks exposing user privacy, makes inefficient use of communication resources, creates data processing bottlenecks, and may lead to concentration of economic and political power. It thus appears most timely to develop the theory and practice of a new form of machine learning that targets heterogeneous, massively decentralized networks, involving self-interested agents who expect to receive value (or rewards, incentive) for their participation in data exchanges.  OCEAN will develop statistical and algorithmic foundations for systems involving multiple incentive-driven learning and decision-making agents, including uncertainty quantification at the agent's level. OCEAN will study the interaction of learning with market constraints (scarcity, fairness), connecting adaptive microeconomics and market-aware machine learning.  OCEAN builds on a decade of joint advances in stochastic optimization, probabilistic machine learning, statistical inference, Bayesian assessment of uncertainty, computation, game theory, and information science, with PIs having complementary and internationally recognized skills in these domains. OCEAN will shed new light on the value and handling of data in a competitive, potentially antagonistic multi-agent environment, and develop new theories and methods to address these pressing challenges. OCEAN requires a fundamental departure from standard approaches and leads to major scientific interdisciplinary endeavors that will transform statistical learning in the long term while opening up exciting and novel areas of research.

Mots-clés : machine learning,federated learning,computational game theory,multi armed bandits,bayesian inference,markov chain monte carlo,multi agent reinforcement learning,online matching markets,data market,incentive
Projet ERC : SHAPE (Horizon Europe, appel 2024, Synergy grants)
Entité participante : Université de l'Indiana à Bloomington — Pays : Etats-Unis — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2701241.0 €

We address a gap in research on the bi-directional effects of language on thought and thought on language, and the developmental pathways that transmit interactions between language and cognition from one generation to the next. The significant empirical advances we propose will result in a unified, cross-disciplinary theory which addresses language diversity through the dynamic interaction of language and cognitive systems across languages and cultures. To this end, we select shape as a core spatial property of entities in the physical world of things which determines bodily action on objects. The abstract properties of shape are codified in multiple ways in the world’s varied languages, including spoken and signed languages – in object and category labels and their organization into classes of categories, spatial prepositions, verbs, and quantifying expressions. Visual shape perception and shape representation in early perceptual and cognitive development are inherently intertwined and interdependent with early language acquisition. Children on atypical developmental trajectories show a cascade of disrupted development in both shape perception and language that can have substantial life-long consequences. Despite this compelling evidence of the pervasiveness of shape, no theoretical account has been offered to explain its nature as a system, neither has this topic been approached integrating evidence and methodologies across disciplines. The main goal of the current endeavor is to provide such an account from the point of view of systems theory and aligning methodologies across vision research, cognitive development, theory of language and language typology and sign language. Our premise is that shape holds a privileged status in human representational systems – in perceiving, using and reasoning about objects and in organizing the structures of the world’s languages and, as such, is key to understanding human intelligence and its developmental trajectories.

Mots-clés : shape perception and representation language development shape bias cross linguistic differences autism developmental language disorder