Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : UltraMeDiQs (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental Constituents of Matter
Entité participante : Université technique de Vienne — Pays : Autriche — Rôle : PI
Acronyme de l'entité : TU Wien
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2311522.0 €

The overarching goal of the project is to develop, test, and explore a novel platform based on a new class of ultracold molecules with unprecedentedly large polar interactions, one to two orders of magnitude larger than for existing ultracold molecular species. This platform will enable a unique series of experiments to study controlled collisions and novel phenomena at the transition from few- and many-body physics.  Molecules with electric dipole moments experience anisotropic long-range interactions and at ultracold temperatures, the quantum nature of these interactions enables applications in quantum chemistry, quantum simulation and quantum computation. The selection of molecules was so far limited to molecules that can be directly laser-cooled or that are composed from ultracold alkali atoms. By contrast, UltraMeDiQs will use optical tweezer arrays as a versatile approach to associate individual ultracold silver (Ag) and cesium (Cs) atoms to molecules. CsAg features an almost ionic bond and therefore a remarkably large electric dipole moment — large enough to create for the first time ultracold molecular ensembles deep in the strongly interacting regime.  The high dipole moment of CsAg makes it also an ideal candidate to investigate so-called field-linked molecules with lifetimes exceeding 10 s. This in turn will enable the deterministic study of (in)elastic collisions involving these states, the formation of weakly bound metal cluster states, and the potential expansion of these clusters to many-body states.  UltraMeDiQs will investigate few- and many-body mesoscopic quantum systems with large dipolar interactions using CsAg as constituents in optical tweezer arrays and optical lattices. The flexibility of the platform and tunability of the dipole interaction will, for instance, enable the simulation of topological optical phonons in a wide range of lattice configurations and the formation of dipolar quantum Wigner crystals.

Mots-clés : ultracold molecules,quantum simulation
Projet ERC : ProtoMan (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : Université de technologie de Darmstadt — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1498151.0 €

For a clean energy future, combustion technologies require profound innovation as they remain essential for providing industrial heat, powering aviation and maritime shipping, supporting heavy-duty applications, and enabling long-term energy storage. To support and accelerate these sectors' transition to carbon-neutral and zero-carbon fuels (such as H2 and NH3), advanced numerical simulations of reacting flows are expected to play an important role. These simulations provide unique insights into the dynamics of turbulent flames in novel gas turbines or industrial furnaces - essential technological devices for the energy transition. However, high-fidelity simulations of turbulent reacting flows demand extensive computing resources, specialized software, and expert knowledge. Due to the multi-scale nature of combustion, even large research institutions face a trade-off between strong simplifications and extensive simulation runtimes, limiting their simulations' applicability for design processes. Manifold-based models offer a promising solution to reduce computational costs without compromising accuracy. By exploiting the self-similarity inherent to reacting flows, these models enable pre-tabulation of thermochemical states instead of repeated recalculations, resulting in significant simulation speed-ups by factors of 10 up to 1,000. Despite this extraordinary potential, there does not exist a straightforward approach to construct these manifolds for general conditions and current methods remain largely heuristic. ProtoMan seeks to unlock the potential of manifold-based models leveraging the synergy between recent advances in 2D flamelet theory and machine learning techniques. Implementing a systematic protocol for data-driven manifold generation, assessment, and utilization, the project aims to make these highly efficient models more accessible, contributing to advancements in energy systems, environmental protection, fire safety, and modern industrial processes.
Projet ERC : ScReeningData (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and Communication Engineering
Entité participante : Université de technologie de Darmstadt — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Data science has quickly expanded the boundaries of signal processing and statistical learning beyond their accustomed domains. Powerful and complex machine learning architectures have evolved to distinguish relevant information from randomness, artifacts and irrelevant data. However, existing learning frameworks lack computationally scalable, tractable, and robust methods for high-dimensional data. Consequently, discoveries, for example, in genomic data can be the result of coincidental findings that happen to reach statistical significance. As long as groundbreaking advances in biotechnology are not accompanied by appropriate learning frameworks, valuable efforts are spent on researching false positives. ScReeningData develops a coherent fast and scalable learning framework that jointly addresses the fundamental challenges of drastically reducing computational complexity, providing statistical and robustness guarantees, and quantifying reproducibility in large-scale and high-dimensional settings. An unprecedented approach is developed that builds upon very recent work of the PI. The underlying concept is to repeat randomized controlled experiments that use computer-generated fake variables as negative controls to trigger an early stopping of the learning algorithms, thereby mitigating the so-called curse of dimensionality. In contrast to existing methods, the proposed methods are completely tractable and scalable to ultra-high dimensions. The gains of developing advanced robust learning methods that are computed ultra-fast and with tight guarantees on the targeted rate of false positives are enormous. They lead to new reproducible discoveries that can be made with high statistical power. Due to the fundamental nature and the broad applicability of the proposed learning methods, the impacts of this project extend far beyond the considered biomedical signal processing use-cases, benefitting all scientific domains that analyze high-dimensional data.

Mots-clés : robust signal processing,biomedical signal processing,high dimensional statistical learning,robust data science
Projet ERC : META-BRAIN (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems E ngineering
Entité participante : Université Sabancı — Pays : Turquie — Rôle : PI
Acronyme de l'entité : SÜ
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Brain metastases have a poor prognosis and are associated with a high morbidity and mortality rate in cancer patients. The metastatic pathways and the role of the blood-brain barrier (BBB) during metastasis are not known. There is a need to better understand cancer brain metastasis and develop effective therapeutics. With META-BRAIN, I will create a human-based in vitro platform that spans the entire cancer cell journey from lung to brain and use it to address three key questions: (1) How does cancer metastasize to the brain? (2) What is the role of the BBB in cancer metastasis to the brain? and (3) How and when can cancer metastasize be treated? The META-BRAIN will provide answers to these questions by developing a truly physiologically relevant human cell-based model, that for the first time, maps the entire cancer metastasis cascade and that can also be used to study novel target proteins at the blood-tumor barrier (BTB). This model will also be used to develop novel shuttle systems for drug delivery to the brain. By integrating state-of-the-art technologies from life sciences and engineering, META-BRAIN has three main objectives: (1) Develop an in vitro model that recapitulates the metastatic cascade with in vivo-like morphology and function to study how cancer metastases to the brain, (2) Investigate the role of extracellular vesicle (EV) and circulating tumor cell (CTC) migration events with a focus on their interactions with brain endothelia, and (3) Identification of novel target proteins specific to cancerous brain and development of novel nano-shuttle systems targeting these proteins to deliver therapeutics to the brain. In summary, META-BRAIN will lead the way in the development of physiologically relevant models for other brain-invasive metastatic cancers, open new avenues for the development of therapeutic and diagnostic approaches and pave the way for nano-shuttle formulations specifically designed to overcome the BBB and BTB.

Mots-clés : cancer metastasis,stem cell differentiation,microfluidic systems,nano shuttle systems,liposome,brain targeting,targeted drug delivery,extracellular vesicle,circulating tumor cell
Projet ERC : ChroMCrust (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth System Science
Entité participante : Université de Copenhague — Pays : Danemark — Rôle : PI
Acronyme de l'entité : KU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499536.0 €

Understanding Earth's crustal growth is crucial to understanding the evolution of its tectonics, the birth of the first continents, and the fundamental changes that transformed Earth into a habitable planet. However, much of our understanding of Earth's crustal growth is predicated on a single mineral - zircon - that is strongly biased towards detecting felsic crustal growth. This is particularly problematic for the early Earth, where average crustal compositions were far more mafic than today, and the very first protocrust may be entirely undetectable using conventional methods. I propose to access the mafic to ultramafic crustal growth record using detrital chromite preserved in sedimentary rocks from Archaean Cratons. Like zircon, chromite chemical compositions reflect the magmas that they crystallised from, and can be used to identify the provenance of the mafic portions of a sedimentary rock. Furthermore, they can be dated using Re-Os isotopes, to identify the age of eroded mafic terranes. This project has three main objectives:  1. Develop techniques to identify the age and composition of chromite sources in ancient sedimentary rocks. 2. Use a range of detrital chromite samples from sedimentary sequences in the Superior Craton, to reconstruct a mafic-ultramafic crustal growth curve for the craton. 3. Search for evidence of Earth's mafic protocrust in some of the oldest known chromite-bearing sedimentary rocks.  These achievements will open a new avenue for studying sedimentary provenance, unlock the archive of mafic crustal growth throughout Earth history, and provide insights on the nature and survival of Earth's earliest crust. The techniques developed will be broadly applicable, paving the way for a better understanding of Earth's crustal evolution.

Mots-clés : chromite crustal growth mafic protocrust mafic and ultramafic magmatism re os isotopes platinum group elements
Projet ERC : ERODE (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Life Science
Panel : Environmental B iology, Ecology  and E volution
Entité participante : Université de Copenhague — Pays : Danemark — Rôle : PI
Acronyme de l'entité : KU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Reducing biodiversity loss is one of the most pressing societal challenges of our time. Population decline has put many species on the path of collapse and extinction. Reducing or reversing this decline is not enough to tackle the associated hidden evolutionary costs, most notably genomic erosion which compromises the long-term viability and resilience of species. ERODE will employ state-of-the-art approaches – (paleo)genomics, quantitative genomics, and evolutionary modelling – to assess the dynamics of genomic erosion on neutral, beneficial, and deleterious variation and understand its effects on organismal fitness, population viability, and extinction risk. I will perform one of the most comprehensive genome-fitness studies to date by examining the fitness effects of genomic erosion over time in 6,530 individuals of three iconic endangered bird species for which I have access to over 30 years of genetic samples and fitness data. Together with zooarchaeological and museum-preserved samples, I will draw a dynamic timeline of genomic erosion in response to population decline. Next, I will develop a modelling framework parametrized and validated with empirical data to predict the consequences of genomic erosion. I will use this framework to understand the efficacy of in-situ and ex-situ conservation strategies in collaboration with The European Association of Zoos and Aquaria (EAZA). The computer model will incorporate genomics into the Green Status of Species to evaluate species recovery and conservation impact, in collaboration with members of the International Union for Conservation of Nature (IUCN). ERODE will produce one of the largest genomic datasets for endangered species in the world and a robust modelling framework for conservation scientists and practitioners. The outputs will be disseminated in academic journals but also over a series of workshops, instruction videos and other outreach activities, which will support their practical application.

Mots-clés : conservation biodiversity genomics modelling predicting forecasting management endangered birds genetic load functional variation neutral variation
Projet ERC : InitialConditions (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental Constituents of Matter
Entité participante : Université de Copenhague — Pays : Danemark — Rôle : PI
Acronyme de l'entité : KU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1496368.0 €

The central goal of heavy-ion physics at the energy frontier is to create, and study in the laboratory, Quark-Gluon Plasma (QGP), a state of matter predicted by the fundamental theory of strong interactions. Current state-of-the-art interpretation of experimental data from the LHC experiments relies on Bayesian global fits of anisotropic flow vn and mean transverse momentum [pT], and provided the first quantitative measures of the fundamental transport parameters (shear and bulk viscosity) of the QGP. This represents the best understanding of the QGP so far. However, recent studies of the correlations between anisotropic flow and mean transverse momentum reveal that no existing Bayesian analysis can describe the new data in a consistent way because of the lack of constraints on the initial conditions, which set the stage for the subsequent dynamic evolution. Hence, it is scientifically urgent to significantly improve understanding about the initial conditions in the various collision types that can be probed at the world’s leading facility, the Large Hadron Collider, to be able to extract precise properties of the QGP and its dynamic evolution as a function of temperature (time). In this ERC project, I will develop methodology for studying genuine correlations between vn and [pT], using a new approach, a multi-particle cumulants technique. This will give unique insights into the initial geometric conditions, shape, size and their correlations and fluctuations. To achieve this, I will measure on various collision systems (129Xe, 16O and proton) during the coming LHC Run 3. These pioneering measurements, and the resulting new analyses, will decisively advance our understanding of those crucial initial conditions, that are the platform upon which the analysis of the entire collision rests. The results of this ERC will make it possible to determine the ultra-precise QGP properties and discover the new physics that could revise our concepts of the initial conditions.

Mots-clés : heavy ion physics,initial conditions,anisotropic flow,new physics
Projet ERC : ThePlantWaterPump (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth System Science
Entité participante : Université de Copenhague — Pays : Danemark — Rôle : Partner
Acronyme de l'entité : KU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 173800.0 €

With global warming, climate zones are projected to shift poleward, and the frequency and intensity of droughts to increase, driving threats to crop production and ecosystems. Plant hydraulic traits play major roles in coping with such droughts, and process-based plant hydraulics (water flowing along decreasing pressure or total water potential gradients) has newly been implemented in land surface models. An enigma reported for the past 35 years is the observation of water flowing along increasing water potential gradients across roots. By combining the most advanced modelling tool from the emerging field of plant micro-hydrology with pioneering cell solute mapping data, I found that the current paradigm of water flow across roots of all vascular plants is incomplete: it lacks the impact of solute concentration (and thus negative osmotic potential) gradients across living cells. This gradient acts as a water pump as it reduces water tension without loading solutes in plant vasculature (xylem). Importantly, water tension adjustments in roots may have large impacts in leaves due to the tension-cavitation feedback along stems. With The Plant Water Pump I will combine for the first time cutting-edge osmotic mapping and micro-hydrological modelling approaches to (1) characterize water status and osmotic responses to water deficit in diverse crop and tree species, (2) revolutionize the current paradigm of plant water uptake, and (3) increase the accuracy of plant water status functions for land surface models. By creating a continuum between key cell-scale variables and plant-scale water fluxes, this project lays the foundations for future multidisciplinary research encompassing plant physiology and ecohydrology. Besides its groundbreaking contribution to the fundamental understanding of plant water relations, this effort embodies a much-needed step toward the accurate forecasting of land water fluxes and decision support under future climates.

Mots-clés : ecohydrology,plant hydrodynamics,multiscale modelling,land surface models,root water uptake,soil hydraulics,cavitation,xylem,water deficit
Projet ERC : LCFlow (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : Université technique du Moyen-Orient — Pays : Turquie — Rôle : PI
Acronyme de l'entité : ODTÜ
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Liquid crystals (LCs) are the delicate phases of matter that exhibit molecular order, fluidic nature and birefringent optical properties. LCs have been developed as materials suitable for energy- and label-free reporting of the chemical changes occurring at their interfaces such as the presence of biomolecular, gaseous or nano-/microscopic species, or the occurrence of the chemical or biochemical interactions/reactions involving these species. LC-water interfaces were employed in most promising sensors as a medium to facilitate the interaction of the LCs with the species. Although promising, the studies reported were limited to the stagnant LC systems, limiting their use in continuous sensing and diagnostic applications. This project is designed to open a new era in the sensing and diagnostic systems involving the use of LCs by introducing a microfluidic flow. The system of interest differs significantly from their counterparts with the introduction of LC-water interfaces that facilitates the exchange of analytical species during flow. However, the design of such system is challenging and critical understanding is required to proceed towards the next generation LCFlow platforms. We aim to design highly sensitive, dynamically tunable, and label-free LC based fluidic sensing platforms and therefore this proposal is structured to understand: 1) The effect of the presence of the "soft" interfaces and the LC interfacial anchoring on the flow regimes, and the LC director profiles, 2) The role of the type, scale, shape and the symmetry of the chemical heterogeneity at the contacting surfaces on the LC flow and configurations, 3) The dynamic influences of the changes occurring at the contact interfaces on the configuration and the optical appearance of the LC medium. The proposal is positioned at the intersection of fundamental knowledge generation and application. It is highly interdisciplinary in nature involving physics, chemistry, materials science and engineering.

Mots-clés : microfluidics,liquid crystals,colloids,interfacial phenomena
Projet ERC : TOCCATA (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and Communication Engineering
Entité participante : Université de médecine de Vienne — Pays : Autriche — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1494125.0 €

Drug resistance, whether intrinsic or acquired during the course of treatment, is the primary cause of cancer treatment failure. Resistance is a complex and highly personalized problem, depending on tumor-specific, genetic, and other factors, but certain cancers are particularly difficult to treat. In particular, the most common and deadliest type of cancer originating in the brain, glioblastoma (GBM), is aggressive and highly resistant to treatment. Due to the complex nature of GBM, advanced methods for screening personalized treatment strategies are critically needed to improve patient outcomes. Cell spheroids are 3D tissue cultures that have proven to be a more accurate model of tumor tissue for therapeutic testing, however they are sensitive to their growth and preparation conditions. Variability in spheroid properties may affect the results of therapeutic testing, so a method of identifying spheroids with properties that match the original tissue is needed. In order to combat GBM and other drug resistant cancers, we propose the development of two novel optical imaging systems. These systems will perform non-destructive characterization of tumor tissue and live monitoring of in vitro drug testing on tumor spheroids. Both imaging systems use a unique design to scan the tissue samples at different angles to quantify a wide range of optical properties within the tissue with 3D micron scale resolution. This approach integrates several different optical coherence tomography-based methods and addresses their weaknesses to create a unique platform for comprehensive measurements of optical properties. This platform will be used to scan the original tumor tissue and the resulting tumor spheroids to identify which spheroids will most reliably mimic the original tumor tissue for testing different therapeutic interventions. This approach has the potential to advance the field of personalized medicine and enable more rapid and reliable development of cancer therapies.

Mots-clés : optical properties optical imaging optical coherence tomography tumor spheroids personalized medicine glioblastoma