Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : DeepRxn (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and Analytical Chemical Sciences
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 : 1499285.0 €

The exploration of reactions is a central topic in chemistry. Compared to the success of machine learning for molecules, the modeling of reactions is lagging behind, especially for stereo- and regioselective reactions. Since current efforts toward sustainable synthesis such as asymmetric organocatalysis or biocatalysis rely on the accurate prediction of enantio- and regioselective reaction pathways, new modeling approaches are needed. The proposed project aims toward developing new, data-driven deep learning frameworks for modeling organic and enzymatic reactions, focusing on chemo-, regio-, and stereoselectivity arising through intermolecular interactions with the reagent, solvent, or catalyst. In detail, we target the rule-free, stereochemistry-aware modeling and subsequent experimental validation of asymmetric organocatalysis to identify new enantioselective transformations, the exploration of new biocatalytic synthesis pathways including enzymatic cascades, and the accurate prediction of activation energies via developing new deep learning approaches. We will expand molecular graph-convolutional neural networks and graph transformers to reactions in a rule-free manner, and introduce hidden three-dimensional representations to account for stereochemistry and intermolecular interactions, yielding a versatile, open-source toolbox for reaction deep learning. This approach largely surpasses current approaches, which rely on two-dimensional representations, reaction rules, or three-dimensional input data, in offering the opportunity to model three-dimensional aspects and atom-mapping on-the-fly, for the first time, representing a significant breakthrough in this field. Its experimental validation campaign further allows for a direct application to the identification of new asymmetric organocatalytic transformations, as well as enzymatic cascades including cofactor recycling and side-product reduction, addressing the current need for more sustainable synthesis.

Mots-clés : machine learning deep learning reaction properties enzymatic cascade design asymmetric organocatalysis
Projet ERC : DeformedNuclei (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental Constituents of Matter
Entité participante : Université de technologie de Darmstadt — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1497360.0 €

Nuclear physics is one of the main drivers for extending our current understanding of Nature, its fundamental forces and the organization of compound systems on subatomic scales. The description of atomic nuclei and nuclear matter connects microscopic systems to astrophysics and the origin of elements, bridging orders of magnitudes in energy scales. A precise understanding of the rich nuclear phenomenology and their emergence from the interaction between neutrons and protons impacts various facets of contemporary physics. Despite tremendous progress over the past decades a fully controlled description of nuclei throughout the entire nuclear chart is still lacking. In particular many experimentally relevant nuclei reveal exotic shapes and strong deformation where nuclear physicists still rely on the use of phenomenological approaches based on uncontrolled approximations with limited predictive power beyond the regions where they have been adjusted.  In my project I will develop new technologies to target deformed nuclei using nuclear interactions derived from chiral effective field theory and study the impact of interaction models on the predicted nuclear shapes. Combined with a uncertainty quantification of many-body observables this allows for unprecedented predictions of nuclei far away from shell closures – significantly extending the scope of first principles nuclear structure calculations. The novelty and challenge of the proposed research lies in (i) the design of many-body frameworks applicable to deformed nuclei, (ii) statistical analyses for uncertainty quantification and (iii) the establishment of tensor network approaches in ab initio simulations. This array of developments, puts me in the unique position to tackle the following big research questions: How does nuclear deformation emerge in a first-principles approach? What are the overall uncertainties associated to an ab initio computation? What is the most efficient way of describing exotic nuclei?

Mots-clés : nuclear deformation nuclear structure ab initio many body theory tensor network theory quantum information theory
Projet ERC : LeMo (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
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 €

We are taking part in a revolution of how people perceive each other and how they communicate in the digital space. Emerging technologies demonstrate how humans can be digitized such that their digital doubles (digital humans) can be viewed in mixed or virtual reality applications. Especially, for immersive telepresence these 3D digital humans are of high interest since people can see each other in augmented (AR) or virtual reality (VR), and can interact with each other. For an immersive experience this requires a high-quality level of appearance reproduction as well as the estimation of subtle motions of the human. Besides quality aspects, digital humans need to be reconstructed and driven by consumer-grade hardware solutions (e.g., a webcam) to enable a wide applicability of digital humans for immersive telecommunication, virtual mirrors (e-commerce), or other entertainment purposes (e.g., computer games). In this proposal, we concentrate on the computer vision and graphics aspects of capturing and rendering (realizing) motions and appearances of photo-realistic digital humans reconstructed with commodity hardware and ask the question:  Can we leverage natural language for the reconstruction, representation, and modelling of the appearance, motion, and interactions of digital humans?  As the face and gestures play an important role during conversations, we focus our research on the upper body of the human, aiming at capturing and synthesizing complete, contact-rich digital humans with life-like motions. We will analyze how humans move and will develop data-driven motion synthesis methods that learn how to generate talking and listening motion with interactions of the hand with the face.

Mots-clés : neural rendering,motion synthesis,language driven reconstruction,3d reconstruction,appearance modelling
Projet ERC : C4 (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Compl exity
Entité participante : Université de technologie de Darmstadt — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499845.0 €

Compositionality and cumulative culture are two foundations of human intelligence that have separately been proposed as "the singular factor" differentiating human cognition from other animals or AI. Compositionality is a core concept in cognitive science, allowing us to solve new problems by recombining familiar elements in novel ways. Cumulative culture is deeply rooted in anthropology, highlighting humanity's unique capacity to massively improve on the wisdom of past generations. Here, C4 (pronounced cee-four in a nod to the explosive compound) seeks to unify these domains under a mathematical framework for compositional compression, offering ground-breaking new predictions by accounting for synergistic interactions between representations. Just as information-theory provided foundational insights across numerous domains, C4 promises broad applications for understanding how humans encode and transmit recombinable fragments of knowledge --- internally within our brains, interpersonally between individuals, and multi-generationally across societal timescales. The goals are threefold: 1) To develop a framework for compositional compression that can explain curriculum effects in individual learning; 2) To predict complementarity in dyadic interactions between diverse individuals; 3) To develop a model of compositional culture, with a causal account of the leaps and bounds that characterize cultural evolution. These goals are only now possible due to recent advances in program induction and large language models, facilitating the transcription of natural language explanations into an explicit and compositional Language of Thought. In sum, C4 proposes fundamental innovations to our understanding of how humans encode and transmit information, where a framework for compositional compression can unite individual cognition with societal evolution under the common understanding of reuse, recombination, and creative re-engineering of past solutions.

Mots-clés : compositionality,efficient coding,curriculum learning,collaboration,cultural evolution
Projet ERC : orthocat (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : University of Wuppertal — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : BUW
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Organocatalytic analogues of transition metal catalysts can future-proof organic synthesis against the depletion of metals, improve the sustainability of chemical production, circumvent catalyst deactivation by Lewis-basic motifs, and avoid toxic metal impurities in drugs. Organocatalytic strategies for the reduction of unactivated C=C bonds and the C–C cross-coupling of unactivated halides, which are among the most common transformations in medicinal chemistry, remain difficult to develop with broad scope as oxidative substrate activation requires highly reactive species. Photocatalysis is a potential solution since it can selectively activate even unreactive motifs under mild conditions. Yet, the generated open-shell species are often difficult to control without a metal co-catalyst. EDA complexes, which have emerged as a photocatalyst-free means for light-mediated reactions, in principle, enable selective transformations without a metal co-catalyst by proximity-control within contact ion pairs. In practice, however, such reactions often depend on a fragmentation step that generates free radicals, which are subject to the same limitations as those generated by classical photocatalysis. Project ORTHOCAT aims to achieve selective organophotocatalytic C=C bond reduction and C–C cross-coupling reactions via rearrangement reactions of contact ion pairs derived from EDA complexes with ortho-disubstituted arenes. The key design principle is the bond polarization upon single electron transfer, which enables a nucleophilic substitution step that mimics migratory insertion or reductive elimination steps of transition metal mediated reactions. The successful development of this proposal will establish an orthogonal catalysis strategy, address the pervasive and vexing deactivation of Lewis-acidic metal catalysts in medicinal chemistry, and enable new transformations to sought-after motifs.

Mots-clés : photochemistry,organocatalysis
Projet ERC : BeyondSTREAMS (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe Sciences
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 : 1686734.0 €

One of the key questions driving astrophysics research today is the nature of dark matter, which comprises 80% of the matter in the Universe. Stellar streams are sensitive to the distribution of dark matter and to the population of dark matter subhalos in galaxies, both of which depend on the mass and interactions of the dark matter particle. My proposed work will use the wealth of incoming stellar stream data materializing over the next five years from the Nancy Grace Roman Space Telescope, the Vera C. Rubin Observatory, and the Euclid Space Telescope to measure dark matter halo masses, shapes and concentrations, as well as subhalo populations of external galaxies. I will lead a fundamental shift in the approach to stellar stream studies through statistical model-to-data comparisons between theoretical predictions from various dark matter candidates (cold, warm, wave-like, self-interacting) and the actual stream data. To achieve this goal, I will develop novel numerical techniques which model and fit multiple streams at once in multiple external galaxies, run state-of-the-art N-body simulations of disrupting globular clusters in dwarf galaxies to place theoretical constraints on the expected substructure, and carry out statistical comparisons between dark matter models and properties derived from the stellar stream data. I will rule out dark matter candidates that are inconsistent with the new stellar stream data, and by the end of the 60-month grant period, I will have the world-leading constraints on dark matter from stellar streams. This work provides an innovative method for mapping the otherwise invisible dark matter, and will constrain statistical properties of dark matter related to its nature and possible extensions of the standard model of particle physics.

Mots-clés : stellar streams
Projet ERC : NovaDePro (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed Matter Physics
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 : 1454635.0 €

Superconducting qubits have emerged as a leading platform for realizing intermediate- and large-scale quantum computing and quantum simulation. This success has been due to the exceedingly wide range of qubit encodings and rich physics attainable by combining superconducting circuit elements to achieve high coherence qubits and high fidelity quantum operations. In this project, I will demonstrate novel approaches to two central aspects of the future of superconducting quantum computing. 1) Despite the dramatic scaling in the number of qubits, the fundamental workhorse to implementing quantum algorithms and quantum error correction is still two-qubit interactions. 2) There has recently been a large interest in novel so-called 'protected qubit encodings' for high coherence, but none have yet been competitive with standard 'non-protected' qubits. The main results of NovaDePro will be -Implementation of a novel qubit-qubit coupling mechanism enabling fast microwave-activated multi-qubit gates. -Demonstration of the first single-shot high-fidelity four-qubit gate and parity readout, enabled by the new coupling technique, in a surface code quantum error correction layout. -A new approach to hybrid superconductor/semiconductor Josephson junctions with high stability (as demonstrated in our recent experiments) and coherence properties compatible with state-of-the-art superconducting qubits. -The first demonstration of superconducting circuits that combine standard insulator-based and hybrid superconductor/semiconductor-based Josephson junctions to implement new high-coherence protected qubit encodings and straightforward quantum control schemes. These achievements will push the boundaries of superconducting quantum computing by opening a new path for high-fidelity error correction in intermediate- and large-scale quantum computing and demonstrate a new family of high coherence protected qubits in a first-of-its-kind hybrid quantum circuit.

Mots-clés : superconducting quantum computing,experimental quantum computing,quantum error correction,novel qubits
Projet ERC : ASPEQT (Horizon Europe, appel 2025, 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 : 2115405.0 €

Light is one of the most powerful tools we have to interrogate Nature and to develop new technologies. However, at its most fundamental level, light is extremely fragile: single photons can be easily lost and their state inevitably erased. Quantum error correction offers a solution to tolerate such noises by encoding quantum information in the entanglement between multiple photons, making quantum states of light resilient to noise. Successfully implementing this would enable advanced quantum applications, such as quantum computations beyond the reach of conventional computers and unconditionally secure quantum networks. However, challenges in generating robust entanglement with photons have so far posed severe limitations to demonstrating photonic quantum error correction. ASPEQT will develop a new generation of quantum photonic devices that can generate entanglement at the scale required to support quantum error correction. It will do so by advancing foundational hardware building blocks through the integration of emerging color centers in scalable silicon photonic circuits. The project will develop novel techniques for embedding and controlling color centers within silicon nanostructures, achieving highly coherent spin-photon interfaces in silicon—a transformative development for scalable photonic quantum technologies. The technology will be benchmarked by generating robust entanglement between multiple photons and spins, culminating in the experimental realization of quantum error correction of logical photonic qubits that can tolerate photon loss, the dominant noise mechanism for quantum light. This project represents a highly promising and innovative approach to scalable quantum hardware, building on the applicant's unique expertise in solid-state photonic devices and quantum error correction with photons. Success will unlock transformative possibilities for scaling quantum technologies, from robust quantum networks to photon-based quantum computers.
Projet ERC : REEXIST (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Texts and Concepts
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 €

The project will address a significant gap in nineteenth-century Scandinavian intellectual history by exploring for the first time the connection between two influential moments: an existential movement in Scandinavian philosophy of religion and the first women’s movements. Even though many key women writers and reformers were early readers of existential philosophy of religion, like that of Søren Kierkegaard, no study has reconstructed this relation. This oversight reflects historical and conceptual challenges in existential thought and methodological challenges in the recovery of women’s contributions to historical canons. REEXIST confronts these challenges through an interdisciplinary research design with analytical tools from intellectual history, existential philosophy of religion, feminist philosophy and theology, and literary theory. I will first conduct a detailed source-historical study to outline a Scandinavian existential movement and its network. This will lay the ground for characterising women’s contributions to existential thought by reading a diversity of texts— from literature, biography, letters to explicitly political, proto-feminist tracts—through existential-phenomenological hermeneutics, narrative analysis, and thematic-conceptual comparative study. These analyses will demonstrate how existential thought informed women’s activist writings as well as how women contributed to existential philosophy and theology. The project will thus break new ground through systematic exploration identifying novel, overlooked sources in existential thought and showing how women’s writings can be considered existential philosophy. This enables REEXIST to reassess entrenched, paradigmatic narratives in the history of existentialism and its role in nineteenth century Scandinavian thought, culture, religion and society, and present new methodological considerations in the ongoing work of recovering women’s contributions in reflective disciplines.

Mots-clés : existential philosophy of religion,contextual theology,feminist theology and philosophy
Projet ERC : PeptidesAndFear (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Université de médecine de Vienne — Pays : Autriche — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499725.0 €

Fear and anxiety are Fear and anxiety are essential for survival. Adequate reactions to anticipated and perceived threats strongly depend on the current state, context and previous experience. What are the neurobiological mechanisms that underlie these adaptive behaviors? While synaptic circuits are the backbone to process threat signals and transform them into behavioral responses, increasing evidence suggests that neuropeptides tune these synaptic circuits in a state- and context-dependent manner, thus allowing for temporal control of excitability and plasticity. Neuropeptide receptors are abundantly expressed in neuronal circuits that regulate fear and anxiety. In fact, recent evidence suggests that most neurons synthesize multiple neuropeptides and, in turn, are regulated by multiple neuropeptide receptors that are expressed in highly cell type-specific patterns. Indeed, genetic alterations in humans as well as pharmacological and genetic experiments in mice indicate important roles for multiple neuropeptides in regulating fear and anxiety and, if dysfunctional, increasing the risk for multiple neuropsychiatric disorders. However, due to the lack of specificity of existing tools, the circuit mechanisms underlying these effects are largely unknown. In this proposal I aim to reveal cell type-specific neuropeptidergic signaling mechanisms for context-dependent regulation of cortical circuit activity, cellular excitability and synaptic plasticity. I will use cutting-edge in vivo imaging and circuit manipulation tools combined with region- and cell type-specific CRISPR/Cas9-mediated genetic ablation to reveal how these neuropeptidergic signaling mechanisms regulate behavioral responses in the context of fear and anxiety. This multidisciplinary approach will allow me to gain unprecedented mechanistic insights into how neuropeptides drive adaptive circuit computations and how their dysfunction underlies maladaptive behavioral and emotional responses to external cues.

Mots-clés : neuropeptides,prefrontal cortex,crispr cas9,g protein signaling,fear and anxiety,gabaergic neurons,ion channels,synapses,neuronal activity,plasticity