Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : PREDESIGNX (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems E ngineering
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 : 1499985.0 €

PREDESIGNX aims to revolutionize crop protection strategies by developing a comprehensive pipeline for engineering durable disease resistance in Solanaceous crops. This ambitious project integrates advanced computational predictions, synthetic biology, and translational research to reduce pathogen-caused losses.  The project is structured around three interconnected work packages: 1. TARGET: Predict effectors and identify their host targets using advanced computational tools and large-scale genomic data. Validate predictions with high-throughput experimental approaches in planta. 2. BIOENGINEERING: Explore natural domain integrations in well-characterized NLR systems, including Pik and Solanaceous NRC sensors. Engineer NLR receptors with altered recognition specificities using rational design principles guided by structural biology and evolutionary analysis. 3. RESISTANCE: Apply bioengineered NLRs to crop protection, focusing on potato. Generate and evaluate transgenic potato lines expressing promising engineered NLRs against important pathogens.  PREDESIGNX represents a significant advance in plant immunity and crop protection. By focusing on natural host protein domains and multiple NLR scaffolds, the project aims to develop a generalizable framework for engineering disease resistance in crops. This approach promises durable resistance while potentially simplifying regulatory pathways. The insights gained will advance our understanding of plant-pathogen interactions and provide innovative tools for crop improvement.  By spanning computational biology, synthetic biology, and evolutionary analysis, PREDESIGNX has the potential to significantly impact global food security by providing new strategies for protecting key crops against devastating diseases. Furthermore, this project will yield fundamental insights into pathogen virulence strategies and the evolutionary dynamics of NLR-integrated domains, revealing the molecular arms race between plants and pathogens.
Projet ERC : MATERIALISE (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Materials 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 : 1499995.0 €

Defects are at the heart of modern electronic materials. Our ability to precisely design and control defects has advanced computing, energy generation, catalysis, and hundreds of other applications contributing over €4 trillion to the global economy. It is therefore surprising that our capacity to a priori rationalise the presence and behaviour of defects is severely limited. While computational tools can now predict defect properties with high accuracy, they are constrained in their widescale application due to the enormous computational costs involved. This presents a major bottleneck in the design, optimisation, and adoption of next-generation electronics, impacting our ability to create new renewable energy devices for the net zero target.  MATERIALISE will discover new energy materials at the forefront of the EU’s innovation strategy including solar absorbers, thermoelectrics, and transparent conductors. This will be achieved by developing and applying a novel method to capture the impact of defects in semiconductors rapidly and accurately. Based on proof-of-principle work where I demonstrated state-of-the-art machine learning approaches can accelerate and circumvent the use of costly computational defect calculations, MATERIALISE, will enable a 4-5 order-of-magnitude increase in the number of materials that can be accurately screened for energy applications. Crucially, I will identify defect design principles that can yield optimisations across the semiconductor sector more broadly.  MATERIALISE will adopt an integrated approach to materials discovery, extending the boundaries of what is possible in computational design through electronic-structure calculations of bulk, surface, interfaces, transport, and synthesisability. My international network of collaborators will validate and build devices for the most promising candidates. This new approach to designing defects in materials will establish my group at the forefront of computational materials science.

Mots-clés : defects,doping,machine learning,density functional theory,energy materials,high throughput
Projet ERC : ASPIRE (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth System Science
Entité participante : Helmholtz-Gemeinschaft — Pays : Allemagne — Rôle : Partner
Acronyme de l'entité : HGF
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1156818.5 €

The new frontier for weather prediction is the so-called subseasonal time scale of two weeks to two months ahead. To take preventive measures at an early stage, reliable forecasts on this time scale are becoming increasingly important for multiple socio-economic sectors. Subseasonal predictability can be gained from recurring patterns in the Earth system. ASPIRE will focus on one of these, namely modes of tropical convective variability. Long-standing systematic errors due to the parametrization of processes in numerical weather prediction models prevent the predictability of these modes from being exploited. Simply running models at a resolution high enough to resolve tropical convection is not feasible due to high computational costs. Taking advantage of three recent developments, ASPIRE will explore new ways to better exploit the intrinsic predictability of tropical convective modes without exhausting the currently available computing resources. The uniqueness of ASPIRE is its cross-disciplinary approach that builds on my experience in atmospheric dynamics and predictability, numerical modeling, and machine learning (ML). First, ASPIRE will identify the source regions and pathways of tropical forecast errors that prevent the intrinsic predictability from being exploited using a new set of subseasonal ensemble hindcasts. Second, ASPIRE will quantify for the first time the added value of locally confined kilometer-scale resolution in the source regions identified before, and generate probabilistic predictions from deterministic forecasts through ML-based post-processing. Third, to enable simulations at kilometer-scale resolution in operations, ASPIRE will develop ML approaches that emulate the integrated effect of the resolved convection in the tropics at substantially reduced costs. If successful, this approach would be a breakthrough towards improved operational weather forecasts at substantially lower computational costs, for a global socio-economic benefit.

Mots-clés : subseasonal predictability numerical modelling tropical extratropical teleconnections
Projet ERC : DUO (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
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 : 1499826.0 €

The central objective of the DUO project is the development of original, well-defined and highly dispersed heterobimetallic supported catalysts using a molecular approach. Its chemical foundation is to take advantage of both (1) metal-metal cooperative effects and (2) site-isolation benefits, in combination, to achieve unprecedented and highly reactive active sites. This will ultimately lead to new bond activation pathways to tackle challenging catalytic transformations. Specifically, I target supported early/late pair-site catalysts, which are ideally suited to promote the heterolytic cleavage of strong C-H bonds. I will take advantage of this unusual reactivity in two classes of catalytic applications: (i) the late-stage isotopic labelling of organic substrates, such as active pharmaceutical ingredients, and (ii) CH4 and light alkanes valorization into higher hydrocarbons under non-oxidative conditions. These catalytic targets are not only scientifically demanding from a fundamental perspective but also possess high potential industrial outcomes. A leap forward with respect to today’s state of the art is required to achieve these goals. I propose to use an interdisciplinary approach at the forefront of current knowledge, combining the benefits of innovative and tailored molecular chemistry with those of surface science and heterogeneous catalysis. I have recently pioneered breakthrough concepts in this hot emerging research area which are a strong guarantee for the success of the DUO project.

Mots-clés : heterobimetallic complexes,surface organometallic chemistry,catalysis,methane valorization
Projet ERC : REPLACE-TAM (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention,  Diagnosis and T reatment of Human Diseases
Entité participante : Universitätsklinikum Hamburg-Eppendorf — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : UKE
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499987.0 €

Pediatric high-grade gliomas are aggressive central nervous system (CNS) tumors with poor prognosis and no curative treatment options. Tumor-associated macrophages (TAM) represent one of the most abundant cell types of the tumor microenvironment (TME), and directly contribute to tumor-promoting and immunosuppressive mechanisms. Novel methods of CNS myeloid cell replacement provide a unique opportunity to effectively modify this critical tumor component. This project aims to overcome current treatment limitations through the preclinical development of adoptive cell therapeutic strategies targeting TAM in high-grade gliomas to reprogram the TME immune state:  Through the depletion of TAM and microglia, combined with the transplantation of hematopoietic donor cells, the TME will be repopulated with graft-derived myeloid cells. We will investigate the resulting immunological and clinical impact in different models of high-grade glioma. To further increase the effectiveness of this approach, we will perturb dysfunctional immune pathways in donor cells. Transplanted gene-edited cells will permanently disrupt the immunosuppressive state of the TME and empower an enduring anti-cancer response by the host’s adaptive immune system. In an orthogonal approach, we will engineer donor cells that – after myeloid differentiation in the CNS – are able to specifically recognize tumor antigens and actively eliminate cancer cells.  Collectively, my group will pioneer a novel treatment modality against brain cancer by replacing the CNS myeloid niche population with hematopoietic cells that can be engineered to withstand mechanisms of tumor resistance and attack malignant targets. The applicability of this approach in different models of pediatric high-grade glioma will emphasize its translational relevance.

Mots-clés : central nervous system,brain neoplasms,immunotherapy,cell therapy,gene editing,myeloid cells,microglia,tumor associated macrophages,hematopoietic stem cells,tumor microenvironment,phagocytosis,neuroimmunomodulation
Projet ERC : EcoIron (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : Université de technologie d'Eindhoven — Pays : Pays-Bas — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

We are on the eve of a worldwide ironmaking transition. Low-temperature electrolysis (/electrodeposition) for ironmaking has recently received increasing attention as it makes direct usage of (renewable) electricity. One problem, fundamental understanding of metal electrodeposition is largely lacking. Nobody truly understands the process. With EcoIron I want to devote my knowledge in experimental and numerical chemical engineering process to the challenge of leveraging the state-of-the-art metal electrodeposition science. Such knowledge has a huge application potential in green steel production and iron-based energy storage. Four work packages (WP) are outlined: 1.to determine the governing mechanism and kinetics of oxide-to-iron electrochemical transformation from systematic experiments and characterization. The outcome will close a long-lasting scientific debate on dissolution-redeposition vs. solid-state reduction mechanism. 2.to unveil the mechanism and conditions for formation and microstructure evolution of iron dendrites using advanced X-ray visualization techniques. The impact of this groundbreaking knowledge stretches beyond the metal production community to the rechargeable battery community. 3.to clarify the roles of particle-bubble transport and species-current transfer via advanced CFD-electrochemical coupled modelling, offering strategies to enable faster conversion. This will lead to a breakthrough in metal electrowinning technologies and an advanced numerical tool for the scientific community. 4.to demonstrate a novel process for continuous production of electrolytic iron powder. This unites, translates, and validates the scientific knowledge from WPs1-3. The combined outcomes in this project constitute a breakthrough in the metal electrodeposition science. EcoIron is a beginning, which lays a robust foundation for more inclusive scientific and technological advance in the field on electrolysis for metal production to drive the sustainability

Mots-clés : metal fuels,electrowinning,iron powder,alkaline electrolysis
Projet ERC : MACxercise (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Materials Engineering
Entité participante : Université de technologie d'Eindhoven — Pays : Pays-Bas — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499950.0 €

There is an increasing clinical demand for sophisticated medical implants and the scientific field of implant technology is exponentially growing. The main challenge is to harness the immune response to such an implant. In this research we use the immune response to our advantage, by using bioresorbable synthetic biomaterials that are gradually replaced by living tissue inside the body. One of the key immune cells are macrophages, which are the gatekeepers for successful implant integration. Strategies to harness the macrophage response focus on isolated biomaterial design features, such as biochemical or microstructural modifications. One major neglected factor is how macrophages sense and respond to mechanical loads, such as cyclic stretch, or ‘macrophage mechanobiology’. Without an in-depth understanding of macrophage mechanobiology, rational engineering of biomaterials is not possible, leading to unpredictable outcomes and ineffective trial-and-error work. With MACxercise, I will leverage my unique multi-disciplinary expertise in bioengineering, biomaterial science and macrophage biology to address these scientific challenges from a new angle in which macrophage mechanobiology is placed center stage. The Main Aim of the MACxercise program is to systematically dissect how macrophages respond to dynamic mechanical cues and to establish how this affects biomaterial-driven tissue regeneration. The transformative strength of MACxercise lies in the concerted spatial and temporal manipulation of the macrophage microenvironment, using sophisticated engineering tools to decouple the mechanical and physical cues to systematically pinpoint how synergistic or conflicting cues dictate key macrophage functions in the biomaterial microenvironment. With MACxercise, my vision is to catalyze the establishment of an exciting new research field across the boundaries of biomaterial science, immunology, mechanobiology, and tissue engineering.

Mots-clés : tissue engineering
Projet ERC : FAIR-RFB (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : Université de technologie d'Eindhoven — Pays : Pays-Bas — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999958.0 €

This proposal will develop a game-changing paradigm to design, synthesize, and functionalize porous electrode materials with far-reaching consequences in electrochemical science and engineering. Focusing on the Fe-air redox flow battery (FAIR-RFB), which holds promise for low-cost, long duration energy storage, I will employ an interdisciplinary approach bridging (electro)chemical engineering, materials science, and computational design to address the following fundamental challenges: (1) I will elucidate the role of the porous electrode microstructure. I will introduce a new methodology that couples evolutionary algorithms with microstructure-informed simulations to predict ideal electrode geometries. A versatile synthetic platform, non-solvent induced phase separation, will be leveraged to synthesize highly controlled 3D microstructures and train neural networks to accelerate the discovery of optimal geometries. (2) I will determine to what extent surface moieties of the porous electrode influence transport phenomena, kinetics, and durability. I will employ electrografting of select molecules to functionalize porous electrodes and impart functional properties (wettability, activity, stability). I will perform nanoelectrochemical imaging to elucidate the role of electrode-coating-electrolyte phenomena. (3) I will develop a novel electrochemical reactor architecture for high-power Fe-air RFBs. Building upon the two previous developments, I will synthesize tailored iron and air electrodes and leverage polymeric bipolar membranes to realize a high voltage and low resistance electrochemical cell. Advanced imaging techniques, i.e. energy- and wavelength-selective neutron imaging, will be employed to visualize reactive transport phenomena during operation, thus helping to address these questions. The novel approaches developed in FAIR-RFB will enable breakthroughs in performance and durability of large-scale electrochemical energy storage systems.

Mots-clés : redox flow batteries,porous electrodes,mass transport,gas diffusion electrodes,electrochemical energy storage
Projet ERC : ReMINDER (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Université de technologie d'Eindhoven — Pays : Pays-Bas — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1494006.0 €

Artificial intelligence (AI) in the form of deep learning is driving unprecedented progress in numerous fields, e.g., for protein structure prediction and organic reaction planning. In drug discovery and chemical biology, such progress is an “evolution” rather than a revolution: several tasks still await to be solved by AI, e.g., accurate structure-activity and activity-cliff prediction, and design of structurally innovative chemical matter. Increasingly complex deep learning approaches are leading to progressively smaller gains in model capabilities, calling for a revolution in AI for drug discovery. The springboard for this project is a striking observation: while novel deep learning algorithms are in continuous development, the input ‘raw’ molecular representations they rely on (e.g., SMILES strings and molecular graphs) have not considerably changed in the last four decades – limiting the amount and quality of chemical information learnable by AI. The potential of capturing more sophisticated chemical information better into a new ‘molecular language’ is still untapped and bears promise to revolutionize molecular AI. ReMINDER will break with traditional approaches and shift the object of study from increasingly complex algorithms to novel molecular representation paradigms for AI. ReMINDER will be an agent of change in the molecular AI landscape, by developing a new representation framework at the interface between method development and experimental validation. ReMINDER will disrupt the potential of AI to (a) navigate complex structure-activity landscapes, (b) design innovative bioactive molecules from scratch, (c) leverage binding pocket information for molecule discovery. By transforming the chemical information captured for AI, we open opportunities to develop more efficient models and solve open scientific challenges. ReMINDER will create the basis for exciting new technology in the field of deep learning for drug discovery, and chemistry at large.

Mots-clés : drug discovery,chemoinformatics,molecular representation,ai
Projet ERC : UTOPIQ (Horizon Europe, appel 2022, 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 : 1595671.5 €

Topological phases of matter emerge from the interplay between broken symmetries and many-body physics and exhibit many fascinating quantum phenomena. Ultrafast switching between different topological phases using light pulses holds the promise for disruptive optoelectronic functionalities, like dissipationless and fault-tolerant logical operations. However, the lack of proper observable being simultaneously sensitive to the local (in momentum-space) topology of the band structure and compatible with time-resolved measurements prevents the real-time monitoring of ultrafast non-equilibrium topological phase transitions. I will address this fundamental challenge by introducing innovative control and measurement methodologies using tailored light pulses in time-, angle- and polarization-resolved extreme ultraviolet photoemission spectroscopy. This approach will enable to follow the ultrafast evolution of the electronic band structure’s local topology, in photoexcited quantum materials. This will represent a major advance in photoemission spectroscopy, by moving from band structure mapping to accessing the dynamical evolution of the Bloch wavefunction of solids.  I will use these novel time- and quantum-state-resolved dichroic observables to investigate the rich non-equilibrium physics underlying ultrafast topological phase transitions occurring on various timescales following impulsive optical excitation using shaped pump pulses: i) during the formation of hybrid light-matter (Floquet-Bloch) states, ii) upon the transient modification of electronic correlations, and iii) following the excitation of coherent phonon modes. UTOPIQ will deliver a dramatically improved understanding of the interplay between the non-equilibrium behaviour and non-trivial topology in photoexcited quantum materials, while further representing a decisive step towards the development of the field of ultrafast ‘on demand’ topology.

Mots-clés : ultrafast,photoemission,topology,quantum materials,2d materials,extreme ultraviolet,floquet,phonons,correlations,broken symmetry,topological phase transitions,berry curvature,femtosecond lasers,nonlinear optics