Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : OLFATASK (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Life Science
Panel : Environmental B iology, Ecology  and E volution
Entité participante : Université Paris-Saclay — Pays : France — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1864575.0 €

Division of labor and task specialization are key elements explaining the remarkable ecological success of human and animal societies. Social insect colonies are characterized by a highly effective division of labor, with workers specializing in brood care early in life and in foraging later in life. Various theoretical models have been proposed to explain division of labor, most prominently the response threshold model, which postulates that individuals differ in their response threshold to task-associated stimuli, and will engage in particular tasks depending on this threshold. While this model has received some experimental support, the current data still do not explain how division of labor is implemented for all different tasks of a social insect colony. How is division of labor implemented? The success of social insect colonies lies in the capacity of all its members to behave in a well-organized manner, which involves elaborate communication among colony members. Accordingly, ants, wasps and bees use a wide range of pheromones, intraspecific chemosensory messages, to regulate almost every aspect of their life. Astonishingly, the role of olfaction, the main sensory modality used by insects, on the division of labor has been greatly overlooked. Using the honey bee Apis mellifera as a model, I propose and will investigate the groundbreaking hypothesis that variation in olfactory perception and processing, especially regarding social pheromones, can give rise to division of labor. I will use a novel approach in social insects, based on newly developed neurogenetic tools. I will compare olfactory perception and processing in the hive’s different worker task groups (nurses, foragers etc.) and will experimentally manipulate the activity of neural circuits involved in pheromone information processing, while following bees’ behavior and task allocation. In the end, Olf@Task aims to definitively establish the role of olfactory perception on the division of labor.

Mots-clés : honey bee,division of labor,pheromone,brain
Projet ERC : CentRed (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Life Science
Panel : Molecules of Life: B iological  Mechanisms , Structures  and Functions
Entité participante : Vlaams Instituut voor Biotechnologie — Pays : Belgique — Rôle : PI
Acronyme de l'entité : VIB
Type d'entité : Recherche
Financement ERC de l'entité : 2000000.0 €

Worldwide, 17% of couples are affected by infertility, with studies showing a steady decline in sperm quality over recent decades. One in three cases remain unexplained, often marked by a sudden loss of the early embryo due to erroneous cell division, and are therefore hard to treat. Case studies link early embryonic loss to defects in the sperm centrosome, a paternally inherited organelle essential for the embryo's first cell division. Unfortunately, a mechanistic understanding of sperm centrosome function is lacking and its contribution to (in)fertility is understudied. CentRed is centred on the hypothesis that the sperm centrosome is remodelled during spermatogenesis to organise the first embryonic cell division, via a pathway known as 'Centrosome Reduction', with many cases of centrosome-related infertility stemming from defects in this transformation. To test this hypothesis, my team and I will use a multi-disciplinary approach, combining in situ structural biology, histology and biochemistry. We aim to achieve the following objectives: 1. In situ Structural Biology. Resolve the molecular changes that take place during Centrosome Reduction and visualise defects occurring in patients with centrosome-related infertility. 2. Histology. Construct a comprehensive timeline of this molecular transition. 3. Biochemistry. Develop an in vitro assay to test and study sperm centrosome function in fertile and infertile male subjects. This pioneering work will be the first molecular study to resolve the fundamental role of the sperm centrosome and reshape our understanding of male-factor infertility. In a broader context, CentRed will result in new approaches to diagnose unexplained infertility and has the potential to vastly improve treatment outcomes.

Mots-clés : centrosome,centriole,centrosome reduction,spermatozoa,spermatogenesis,gametes,fertility,cryo electron tomography,fib milling,sub tomogram averaging
Projet ERC : QFT.zip (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental Constituents of Matter
Entité participante : ARMINES — Pays : France — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 938705.0 €

Many-body quantum systems with strong correlations are particularly difficult to understand in the continuum, where non-perturbative techniques are in scarce supply. Direct diagonalization methods are not available, since the Hilbert space is simply too large to be manageable. This inhibits progress in high energy physics, nuclear physics, and in the study of exotic topological phases of matter. On the lattice, tensor network states, a variational class of wavefunctions coming from quantum information theory, have allowed to compress exponentially large Hilbert spaces down to a smaller numerically manageable corner. This has allowed substantial theoretical and numerical advance on the many-body problem on the lattice.  This project will develop continuous tensor network states, a new framework to extend the recent lattice progress to the continuum and quantum field theory (QFT). The originality of the approach is that it will not rely on any discretization of space-time. I will work directly in the continuum, without any cutoff. Low energy states of quantum field theories, which a priori live in a continuously infinite dimensional Hilbert space, will be compressed down to a finite and small number of parameters. This will then allow to solve numerically very generic (non-integrable) strongly coupled theories in a fully non-perturbative manner. Such a compression was long thought to be impossible, in particular in the relativistic case, but I overcame crucial theoretical hurdles in the past year, making the proposal particularly timely.  I will construct this framework with 3 main applications in mind: i) non-relativistic problems in 2 space dimensions and more, including e.g. fractional quantum Hall states, ii) relativistic QFT, starting with 1+1 dimensional toy model and gradually increasing complexity to get closer to nonabelian gauge theories, iii) critical quantum systems (and classical statistical mechanics).

Mots-clés : tensor networks,quantum field theory,variational wave functions,entanglement
Projet ERC : MINIONS (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : Istituti di Ricovero e Cura a Carattere Scientifico — Pays : Italie — Rôle : Partner
Acronyme de l'entité : IRCCS
Type d'entité : Organismes privés (hors enseignement)
Financement ERC de l'entité : 100000.0 €

External-beam radiotherapy has been established as best practice care in different cancer cases, although current models applied in the clinics to tune the treatment plan dosimetry, as a function of the expected tumour response and radiation-induced toxicity, do not make use of patient-specific information, but rely on radiobiological parameters typically derived from in-vitro experiments, thus abdicating from describing the in-vivo complexity of the pathology. MINIONS is a cutting-edge research programme that investigates and integrates a set of activities to implement patient-specific microstructural and radiobiological models in personalized radiotherapy treatment planning and adaptation towards a detailed description of tumour characteristics of each patient and thus significantly improved tumour control probability. The main challenge of MINIONS is to create, for the first time, a real-time patient-specific model able to describe the microscopic characteristic of the tumour and its interaction with the radiation beam. The scientific activities of MINIONS are to achieve and merge: (i) Monte-Carlo (MC) simulations of Diffusion Magnetic Resonance (DW-MR) signals and radiation-tissue interactions on a library of in-silico cellular substrates to obtain an integrated simulation platform; (ii) real-time Artificial Intelligence-based techniques to accelerate MC simulations and make them compatible with fast treatment planning and adaptation; (ii) advanced DW-MR imaging, to retrieve non-invasively the information required to feed the simulation platform for the implementation of patient-specific microstructural and radiobiological models. The design of in-vivo and ex-vivo tests allows validating the model performance and the collaboration of researchers, engineers and clinicians will drive the use of this innovative strategy in clinical routine, to increase survival and quality of life for a wide range of cancer patients.

Mots-clés : personalised radiotherapy,microstructural and radiobilogical modelling in radiotherapy,diffusion mri in radiotherapy,monte carlo simulations in radiotherapy,artificial intelligence in radiotherapy
Projet ERC : SuperStoc (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed Matter Physics
Entité participante : Centre national de la recherche scientifique — Pays : France — Rôle : PI
Acronyme de l'entité : CNRS
Type d'entité : Recherche
Financement ERC de l'entité : 1477856.0 €

The dynamics of biological systems, from proteins to cells to organisms, is complex and stochastic. To decipher their physical laws, we need to bridge between experimental observations and theoretical modeling. Thanks to progress in microscopy and tracking, there is today an abundance of experimental trajectories reflecting these dynamical laws. Inferring physical models from noisy and imperfect experimental data, however, is challenging. Because there are no inference methods that are robust and efficient, model reconstruction from experimental trajectories is a bottleneck to data-driven biophysics.  I will bridge this gap by developing practical algorithms that permit robust and universal inference of stochastic dynamical models from experimental trajectories. To this aim, I will build data-efficient tools to learn stochastic differential equations and discover physical models, employing methods from statistical physics and machine learning. The main focus of SuperStoc will be in resolving models with high precision from limited trajectories. To assess the efficiency of the methods I develop, I will design information-theoretical frameworks to quantify how much can be inferred from trajectories that are short, partial and noisy. The convergence of the resulting algorithms will be backed by mathematical proofs and numerical simulations in realistic conditions.  I will apply these new tools to several key open biophysical problems where existing methods are failing: condensate-mediated interactions between genomic loci, cellular mechanosensing in confined environments, pattern formation in embryo development, and visual interaction between fish leading to collective motion.  The resulting algorithms will be implemented into a software designed to be useful for the broad soft biological matter community. By proving that one can do more with the same data and providing tools to do so, SuperStoc will help bridge the inference gap towards data-driven biophysics.

Mots-clés : stochastic processes dynamics of biological systems inference and inverse methods data analysis algorithm development cell dynamics
Projet ERC : MEMOPROSTHETICS (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and Communication Engineering
Entité participante : Centre national de la recherche scientifique — Pays : France — Rôle : PI
Acronyme de l'entité : CNRS
Type d'entité : Recherche
Financement ERC de l'entité : 1499625.0 €

Cognitive deficits caused by ageing, neurodegenerative diseases or brain injury represent a major public health issue. They are associated with a disruption of neural oscillations in large-scale brain networks, which cannot be restored using current treatments. Here, I propose a new neuromodulation framework for manipulating neural oscillations across large-scale brain networks to improve learning and memory. Specifically, I will create a large-scale neuroprosthesis of the hippocampus-prefrontal cortex circuit, which will simultaneously record and stimulate electrically key areas of the episodic memory network. I hypothesize that spatially, spectrally and temporally specific stimulation protocols within this network will enhance the functional interactions between hippocampus and prefrontal cortex, and will improve learning and memory. This conceptual approach will be evaluated in non-human primates trained on a visuospatial learning task.  First, I will combine an electrocorticographic grid over the prefrontal cortex with depth macroelectrodes in the hippocampus and entorhinal cortex to create a novel large-scale brain implant. I will use this device to investigate the neural signatures of successful vs failed trials. Next, I will optimize electrical stimulation protocols that replicate successful neural signatures, and will test whether they improve memory in healthy animals. Third, I will induce temporary cognitive deficits in the animals using a cholinergic antagonist, and will assess the effects of stimulation in this pharmacological model of dementia. Finally, I will evaluate the chronic feasibility of this approach by translating these concepts into a fully implantable and wireless platform suited for future clinical applications. MEMOPROSTHETICS will lead to the development of neuroprostheses that palliate memory deficits in preclinical models of ageing and dementia, and will open new avenues for studying and treating a wide range of cognitive impairments.

Mots-clés : neurostimulation,neuroprosthetics,brain machine interfaces,memory,non human primates
Projet ERC : CODEC (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Compl exity
Entité participante : Radboud University Medical Centre — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : Radboud UMC
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Cognitive ability, measured through standardized tests, provides a highly predictive measure of lifespan outcomes including academic achievement, job success, as well as mental and physical health. However, these cognitive snapshots omit a crucial aspect of cognitive ability: Short term variability in cognitive performance. Individuals with more variable performance are more likely to be mis-stratified into schools or careers with potential lifelong consequences, and more likely to perform at levels that necessitate intervention for periods of time. Moreover, variability reflects a promising early warning marker of adverse outcomes, above and beyond mean performance. However, the challenges involved in measuring variability have left crucial questions unanswered: Is variability a single trait, or does it have distinct factors? What are the neural and behavioural determinants of cognitive variability? What is the association between short term variability and long-term outcomes? In this unique longitudinal design using gamified versions of classic cognitive domains I will measure variability across a range of tasks at multiple levels of temporal resolution: months, days, occasions and trials. 600 children (200 in the neuroimaging arm) will be measured for period of three years. Once per year they will take part in a burst: A week where they will be measured three times a day. I will use using cutting edge methodology to understand the behavioural, neural and environmental mechanisms of variability, as well as the longitudinal consequences of variability on cognitive development and the emergence of mental health symptomatology such as ADHD. By combining the strengths of deep phenotyping with cutting edge quantitative modeling, I will be able to test and develop theories of cognitive development, demonstrate the role of brain structure and function in supporting cognitive dynamics and determine the effect of cognitive variability on developmental outcomes.
Projet ERC : NewRecords (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe Sciences
Entité participante : Centre national de la recherche scientifique — Pays : France — Rôle : PI
Acronyme de l'entité : CNRS
Type d'entité : Recherche
Financement ERC de l'entité : 1500000.0 €

It has recently been established that the current expansion rate of the universe (i.e, the Hubble constant) as measured by local probes is discrepant at 5σ with that predicted in the standard Λ cold dark matter (ΛCDM) model calibrated onto cosmic microwave background (CMB) data. Additionally, analyses of the latest galaxy weak lensing surveys find an amplitude of density fluctuations on large scales (the S8 parameter) that is about 2−3σ lower than what is predicted in ΛCDM. This growing “crisis” is potentially the first indication of a departure from the ΛCDM model, giving hopes to access the fundamental properties of the mysterious dark matter and dark energy that pervade the universe. On the one hand, I have shown with my colleagues that the “Hubble tension” could indicate the presence of new physics in the era pre-recombination, potentially connected to new dark energy properties at early time. On the other hand, the “S8 tension” can be connected to new dark matter properties, and in particular could indicate that dark matter is not stable on cosmological timescales, or interacts with some new light species. Yet, none of the suggested models have been robustly detected nor are they able to explain simultaneously the Hubble and S8 tensions. With the support of the ERC, I will build a group that will develop public tools to firmly identify the new physics mechanism required to explain these cosmic tensions, assess the observational consequences for a wide variety of observables, and the implications for the nature of dark matter and dark energy.
Projet ERC : ONSET (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : Imperial College London — Pays : Royaume-Uni — Rôle : PI
Acronyme de l'entité : ICL
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1498820.0 €

A century of exhaustive turbulence research has allowed the development of a wide range of turbulence closure models, analytical parametrisations and scaling laws, which enter virtually all design and modelling protocols that involve high Reynolds number flows. Closer inspection, however, reveals that only two extreme and polar-opposite turbulence regimes have been well-understood and modelled. When turbulence is highly-strained and evolves rapidly, or when it is lowly-strained and evolves slowly. The in-between regime of intermediate strain, perhaps the most relevant for engineering and environmental applications, remains obscure.  This proposal is about developing and validating a theory for the intermediate-strain turbulence regime, based on the conjecture that it is governed by a universal flow-behaviour, termed ‘rapid self-similarity’, which combines elements from both the high- and low-strain regimes. The proposed investigation is based on three developments. First, the accumulation of evidence in the literature that intermediate-strain turbulence dynamics may accept an analytical description known as the ‘new dissipation law’. Second, the development of machine learning techniques which allow the extraction of physical insights directly from data. Third, the attainment of mature experimental and numerical simulation methods in fluid mechanics, capable of resolving the spatio-temporal properties of turbulent flows. The impact of ONSET is potentially very high, as it will improve the understanding and modelling of a wide range of applications of engineering and environmental science connected to intermediate-strain turbulence. ONSET will demonstrate that by focusing on two example applications: improvement of wind energy harvesting via enhanced wind farm flow modelling, and increase of Unmanned Aerial Vehicle flight efficiency and duration, by making use of UAV group aerodynamics.

Mots-clés : turbulence theory,experimental fluid mechanics
Projet ERC : ATOMQUANT (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed Matter Physics
Entité participante : Helmholtz-Gemeinschaft — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : HGF
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2260965.0 €

The quantum nature of a physical system often emerges from its fundamental building blocks and demands a profound understanding to harvest its advantages for quantum devices. Their design requires knowledge about the atomic-scale environment of a quantum object to protect it from noise and energy exchange. For that purpose, spins on surfaces studied and controlled by a combination of scanning tunnelling microscopy (STM) and electron spin resonance (ESR) has emerged as an atomic workbench that permits to resolve quantum systems such as single atoms and molecules. However, up to now the coherent properties, in particular the phase coherence time, remain much worse than in other quantum architectures such as nitrogen-vacancy centres in diamond or superconducting qubits. In ATOMQUANT, I aim to improve the coherent properties of spins on surfaces by several orders of magnitude. The main goal is to create a new atomic-force microscopy (AFM)-based architecture for quantum information processing and magnetic sensing operating on the atomic scale. I aim to achieve this goal i) by improvements in instrumentation: Here, I will set up single spin resonance in a milliKelvin-AFM setup, based on the technique of magnetic exchange force microscopy. ii) by addressing remote substrate nuclear spins and by utilizing them as a highly coherent quantum resource. This will here be realized by 25Mg nuclear spins that are found in the commonly used magnesium oxide substrate iii) by overcoming major challenges for the detection of spins on surfaces such as requirements for low temperatures and hurdles for device integration. The results of ATOMQUANT will have the potential to bring quantum research to the atomic limit, allowing for exploration of potential qubit systems with outstanding quantum properties in-situ and atom-by-atom. My previous extensive experience in the field of ESR-STM provides the best possible conditions to conduct this innovative high potential research endeavour.

Mots-clés : atomic force microscopy,scanning tunneling microscopy,electron spin resonance,single molecular magnets,quantum information,processing,quantum sensing,qubits,coherent manipulation,nuclear spin resonance,spins on surfaces