Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : HighDimProbComb (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : Université de Cambridge — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499408.75 €

This project concerns three related topics concerning high dimensional probability and combinatorics. The first part concerns fundamental questions concerning random matrices. These include the problem of finding sharp bounds on the singularity probabilities for discrete random matrices; and determining the limiting spectral distributions of ``sparse'' matrix models. The second major goal of this project is to make progress on the hypergraph Ramsey numbers and the multi-colour Ramsey numbers. These are two absolutely central objects in combinatorics which remain mysterious in many respects, despite many years of intense activity. Finally, I propose to study the Chowla cosine problem. This is an old problem in the area of harmonic and Fourier analysis which the PI has recently been studying through a probabilistic lens.
Projet ERC : HyperScaleEM (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed Matter Physics
Entité participante : Université Friedrich-Alexander d'Erlangen-Nuremberg — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2300549.0 €

Electron microscopy is essential to understanding structure-property-function relationships in modern materials engineering, condensed matter physics, chemistry, and structural biology. Yet, due to complicated scattering physics, today’s electron microscopes can only image tiny volumes with 3D atomic resolution. Within this project, I will turn the tables by utilizing and inverting the scattering physics to image scale-bridging volumes with atomic detail and chemical superresolution. Combining compressive data-acquisition protocols, state-of-the-art electron optics and detectors, and co-designed computational imaging algorithms will make this possible. I will use tomographic experiments and computationally invert the multiple scattering from multidimensional measurements in scanning transmission electron microscopy to determine 3D atomic structure and chemistry in technologically valuable volumes. This has not been realized yet due to significant bottlenecks in the computational complexity of the underlying algorithms and a lack of experimental automation, which I plan to overcome in this project. The project is divided into three main objectives: 1) Imaging 3D atomic structure in large volumes 2) Visualizing atomic chemistry in scale-bridging volumes 3) Profiling 3D atomic structure, chemistry, and dynamics in controlled in-situ experiments across scales These methods will be applied to essential materials, including examining single hydrogen atoms at grain boundaries in structural metals and studying concealed, extensive interfaces in modern semiconductor materials. In the final phase, I will record atomistic movies of material fracture in tungsten and alloys. Fracture is one of the most critical failure modes of structural materials with catastrophic consequences. The details of crack nucleation and propagation through the breaking of bonds are still largely unexplored and will be measured directly and compared with large-scale atomistic simulations.

Mots-clés : nanophysics,electron microscopy,large scale inverse problems,scattering physics,computational imaging
Projet ERC : BiosenSAI (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems E ngineering
Entité participante : University of Regensburg — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499453.0 €

The ability of cells to sense and respond to signals is an essential requirement of life. Genetically encoded biosensors meet this need by detecting, for example, chemicals and triggering gene expression in response. This concept is used across the life sciences to sense molecules in basic research, diagnostics and treatment. Crucially, biosensors can be used to isolate and engineer microbes that sustainably produce value-added chemicals and thus play a key role in the transition to a circular economy. However, native biosensors are mostly unfit for synthetic applications in terms of molecules and concentrations they respond to. Moreover, little is known about the relationship between biosensor sequence and resulting function, which prohibits rational biosensor engineering and enforces tedious, often unsuccessful trial-and-error approaches. I propose to build a pipeline for the rational engineering of biosensors with tailored sensory properties to overcome these limitations. Building upon an ultrahigh-throughput DNA-recording technique we have recently invented, we will generate hitherto inaccessible datasets linking over 10^8 transcriptional and translational biosensor sequences with their sensory properties and use these data to train deep learning models that infer biosensor function directly from sequence. This will enable straightforward biosensor design, which we will capitalize on to build a versatile biosensing platform to specifically detect and discriminate molecules from three metabolic compound classes with high potential for bio-based production. Finally, we will apply designed biosensors to engineer new enzymes for CO2-fixation and build dynamic metabolic controllers to obtain superior bacterial strains for the production of flavors and pharmaceuticals. Our novel, data-driven approach will break new grounds in biosensor engineering through synergies between synthetic biology and artificial intelligence paving the way to novel, sustainable bioprocesses.

Mots-clés : sequence function mapping,biosensor,transcription factor,riboswitch
Projet ERC : VOLARE (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Soci al World and Its Interactions
Entité participante : Université d'Anvers — Pays : Belgique — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1841705.0 €

Income targeting has permeated social policy design in European welfare states, in order to boost their efficiency and effectiveness. Yet real-life examples of integrated income-targeted schemes highlight the shortcomings of an overly technocratic view. Social policy research needs to analyse how policy design interacts with the month-to-month income variability experienced by households, and the strategies they develop to cope with and navigate this volatility.  VOLARE disentangles the relationship between fluctuating incomes, household navigating strategies and social policy design in different institutional contexts. VOLARE’s theoretical framework argues that a) households employ both formal and constrained strategies in the face of volatile incomes, that can be focused on short-term juggling of incomes, or oriented towards longer-term objectives; b) the welfare state context and households’ preferences for stability influence the adopted strategies, but c) with their strategies households also actively navigate the eligibility and entitlement criteria determining the accessibility, timeliness and effectiveness of social policy responses. VOLARE assesses the implications of this interaction for effective social policy design.  The framework is scrutinized empirically within an innovative mixed-methods design that links consecutive quantitative monthly incomes collected among over 240 low to middle-income families in four European countries, with in-depth interviews on the strategies selected households develop to navigate volatile incomes. These findings are confronted with nationally representative month-to-month volatility estimates and welfare state responses. A survey experiment adds further substance as to how households value stability over income, given their access to formal or constrained navigating strategies.  In doing so, VOLARE forces a major breakthrough in the research on income volatility, social policy effectiveness and household strategies.

Mots-clés : income volatility,social policy design,timeliness,targeting,navigating strategies
Projet ERC : TEMPER (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : Université d'Anvers — Pays : Belgique — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1498730.0 €

TEMPER sheds new light on the representative behavior of individual politicians by addressing an overlooked driver of that behavior: their emotions. What makes politicians decide to be responsive to public opinion signals at some occasions, but not at others? And how do politicians go about communicating their viewpoints, sometimes using very emotional or uncivil language, and at other times not? Whereas extant research has been preoccupied with studying the cognitive, strategic reasons that politicians may have to act so-or-so, TEMPER focuses on the emotional explanations of their behavior. It sketches a portrait of politicians who are, above everything, human.  Doing so, TEMPER bridges the gap between theories of elite behavior and theories of the political behavior of citizens, which have—relying on insights from psychology—taken an ‘emotional turn’ decades ago. As emotions arise in response to concrete situations of signals, TEMPER pays attention to the situations or signals that provoke politicians to communicate and act. Its objectives are to explain (1) how characteristics of situations or signals elicit emotions in politicians, (2) how these emotions influence their subsequent representative behavior, and (3) how the resulting ‘emotional explanation’ of behavior maps onto existing (mostly cognitive) explanations of behavior.  TEMPER studies the emotions of politicians directly, in a series of (1) survey-experiments with politicians; (2) interviews with politicians; and (3) direct observations of actual media debates and party meetings. It combines self-reported measures of emotion with psychophysiological measures of affect, which are less susceptible to rationalization and social desirability. The findings of TEMPER will reveal how patterns that we currently interpret as strategic behaviors may, in fact, be grounded in emotional mechanisms.

Mots-clés : political representation political communication political behavior emotion
Projet ERC : ENDINGS (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Human Mobility, Environment, and Space
Entité participante : Université de Lund — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1482076.0 €

Because innovations are important to achieve sustainable development, substantial investments have been made in innovation research. Yet, innovations often prove insufficient for replacing established production and consumption practices. In ENDINGS, I argue that we need to overcome this innovation bias by studying how existing socio-technical systems and their respective knowledge bases end – or not. In fact, evidence suggests that it is more likely for existing systems to endure than to disappear. Still, we have a rather limited understanding of how decline and de-institutionalization unfold. This is an important research gap in several disciplines that needs to be addressed to tackle the transformations needed to achieve a more sustainable society. Combining theoretical insights from innovation and organization studies in a novel way, I will study the epistemic motors of industries by developing an analytical framework that 1) introduces a knowledge-based conceptualization of socio-technical systems; 2) differentiates between different kinds of knowledge and their prevalence in certain industries; 3) specifies the ways in which knowledge is maintained and reproduced; 4) identifies the mechanisms that lead to the erosion and extinction of knowledge; 5) substantiates endings as a precondition for innovation and systemic change. To apply this framework, I will re-formulate the method of ‘innovation biographies’ by adding the phases of erosion and extinction to the knowledge life cycle and pioneer its application in comparative cross-case analyses of three industries (energy, ICT, craft) in three geographical contexts (Germany, UK, Sweden). Based on the findings, I will formulate an original, interdisciplinary theory of endings that understands innovation as only one part of the knowledge life cycle. Such a novel perspective will open new research avenues for empirical analysis that will significantly advance research on innovation and sustainability transitions.

Mots-clés : innovation studies sustainability transitions institutional theory socio technical systems de institutionalization
Projet ERC : BREAK-OUT (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Life Science
Panel : Physiology in Health, Disease and Ageing
Entité participante : Université d'Anvers — Pays : Belgique — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1494848.0 €

Thoracic aortic aneurysm (TAA) entails a high risk for aortic dissection and rupture, which is a prominent cause of death in Western countries. Prophylactic surgery significantly reduces the mortality risk, but complications are relatively common. Moreover, in severe TAA conditions aneurysms often develop at other locations afterwards, exposing patients to repeated surgeries and, thus, threats. Current drug options only modestly slow down dilatation, without preventing dissections or ruptures. Clearly, a game changer in TAA patient management would be the availability of medical therapies capable of stopping or reversing aneurysm formation. Functional characterization of the known TAA genes, especially those that are linked to syndromic TAA, in relevant cell and/or mouse models has already delivered valuable insights into the disease mechanisms, prompting pre-clinical drug testing in mice. The mechanistic picture is incomplete though, encumbering the development of additional, and especially more effective, therapies. Another prevailing issue is the inefficient and/or unsuccessful translation of pharmacological mouse results to the clinic. Few compounds make it to clinical trials due to the high costs, lengthy time frames and difficulties as to patient recruitment. Additionally, while TAA mouse models allow us to study and therapeutically target disease in an in vivo setting, efficiency of ensuing compounds might not be recapitulated in humans. Building on intriguing preliminary data and the unique availability of mutant/control Fbn1 and Ipo8 mice and human induced pluripotent stem cells, this project aims to contribute to the resolution of these issues by further unravelling and therapeutically targeting the mechanisms underlying syndromic TAA. Additionally, BREAK-OUT will provide proof-of-concept that patient-derived aorta-on-a-chip models can be used for pre-clinical TAA research.

Mots-clés : thoracic aortic aneurysm and dissection,drug discovery,aorta on a chip,transcriptomics,transgenic mouse models
Projet ERC : 4DLang (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : University of Technology Nuremberg — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : UTN
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1563730.0 €

We have recently experienced a boost in AI as the performance of ChatGPT-like large language models has matured from a purely scientific endeavor to deployment in various businesses and real-world applications. Also in computer vision, we have seen tremendous gains that were enabled by scaling to large models trained on vast corpuses of data in an unsupervised fashion. Language is symbolic and can inform about abstract properties and relationships, while vision without human labels does not model explicit semantics and brings distributed representations for spatial structures. Both are complementary, and the fundamental unsolved challenge is to bring them together. The current state of the art is to follow the common paradigm of scale and to naively train models on large amounts of data to exploit the co-occurence of objects in single images and words in text captions to learn their correlation. However, looking at the outputs of these models reveals that they in fact perform extremely poorly in many cases. The next step to approach human-level AI requires reasoning about scenes spatially and semantically at the same time and demands an abstraction of our real world that brings both of these modalities together, while being lightweight and highly efficient. 4DLang presents the solution and introduces a new approach by first creating a primitive-based geometric symbolic abstraction of physical scenes that is then shaped into a spatio-temporal language. It will enable the fine-grained coupling of both modalities and go beyond the state of the art by augmenting large language models with real-world understanding that is only present in observations of moving scenes, as we humans perceive them. This design will fundamentally advance the generalization abilities of AI and have a large impact on downstream applications, such as content interpretation and generation, AI assistants, robotics, and autonomous driving.

Mots-clés : 3d computer vision,3d reconstruction,graphics primitives,large language models,transformers,detection,tracking,open vocabulary segmentation,text to image generative models,incremental learning,learning behavior,object representation,scene representation,world models
Projet ERC : PRIGLUE (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Université de Münster — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : Universität Münster
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 958437.5 €

Protein-RNA interactions play a key role in cell biology and there are limited tools for specific modulation. In this project we will look for molecules that act as glues by stabilizing the interaction between an RNA-binding protein and a specific RNA sequence which we call PRIGLUEs. The aims are to explore key concepts of how to develop screening methods to identify such PRIGLUEs and to determine what type of chemical matter is suitable for this approach. These novel molecular glues will be evaluated for their RNA selectivity and the mode of interaction stabilization. The methods we will develop to identify PRIGLUEs can be applied to any protein-RNA interaction, and we will use them to modulate the process of mRNA splicing to prove the concept. The glues will be applied to stabilize mutant splicing factor – RNA pairs to recover lost affinity and correct disease-causing splicing outcomes. PRIGLUEs will be applicable as novel tools for RNA biology studies but can eventually also lead to therapeutics with an unexplored mode of action.

Mots-clés : molecular glues,protein rna interactions,splicing
Projet ERC : NEFELI (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and Communication Engineering
Entité participante : Université de Gand — Pays : Belgique — Rôle : PI
Acronyme de l'entité : UGent
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1457750.0 €

Efficient assessment of multiple bio/chemical and electrophysiology biomarkers directly at the area of interest is an indisputable asset in the individualized healthcare. However, current implantable systems and bioelectronic technologies still face limitations in ultra-sensitive bio-sensing, address the electrical and chemical aspects fragmentally, and depend on complex setups and computationally heavy off-line processing. Conventional von Neumann architectures face limitations in efficiently handling the increasing sensor output data that can be mainly attributed to the physical separation between sensing, memory, and computing units. Here the overall objective is to conceptualize a first-of-its-kind, miniaturized, and self-contained biosensing technology employing neuromorphic devices functioning as on-node sensors and processors (in-memory (bio)sensing and computing), in soft, flexible and bio-compatible materials and format. I will demonstrate this technology showcasing, in an in vivo animal model, a proof-of-concept implantable bio-interface that intelligently interrogates and classifies neurodegenerative disease-related bio/chemical and electrical biomarkers, coupled with active elements enabling precise adjustment of stimulation control parameters based on analog inputs. This technology holds great potential to advance our understanding and treatments of pathologies through multiplex electrical and chemical monitoring reducing the demands for power-intensive analog-to-digital conversion and computational processing. Furthermore, it paves the way for tailored interventions, laying the foundation for next-generation biomedical modulation systems.
Projet ERC : URENIMOD (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and Communication Engineering
Entité participante : Université de Gand — Pays : Belgique — Rôle : PI
Acronyme de l'entité : UGent
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499575.0 €

Epilepsy is a common and debilitating chronic neurological disorder. Furthermore, a large fraction of patients suffers from refractory (drug-resistant) epilepsy. Responsive electrostimulation is a state-of-the-art treatment for refractory epilepsy but is invasive and not cell-type selective. As a result, the surgery is susceptible to complications, e.g., haemorrhage and infection, and overstimulation due to false-positive epilepsy detection can cause brain region overactivity and even induce seizures. In contrast, ultrasound neuromodulation (UNMOD) is a recent, non-invasive and cell-type selective technique, that promises to pave the way for truly inhibitory protocols. The goal of this project is to design and optimize a first-of-its-kind minimally invasive closed-loop UNMOD treatment for refractory epilepsy. Acousto-electric heterodyning will be used to realize remote non-invasive hippocampal neurorecording. Subsequently, a controller will detect seizures and will trigger neuromodulatory insonication with a miniaturized subcutaneously implanted phased array. Moreover, an alternative non-invasive and closed-loop technology will be designed, for testing and patient selection before implantation. The main hurdle towards the realization of this disruptive treatment is our limited understanding of the underlying mechanisms of UNMOD and acoustic neurorecording. I intend to close this knowledge gap by developing an experimentally validated, comprehensive and morphologically-realistic computational framework of the ultrasound-sensitive hippocampal formation. Subsequently, this model will be used to optimize the insonication and recording protocols and to design the transducer and electrode arrays. This project concentrates on focal epilepsy, but the envisioned minimally- to non-invasive, cell-type specific, closed-loop technology with millimeter resolution, has the potential to revolutionize also the treatment of other brain and peripheral nerve disorders.

Mots-clés : ultrasound neuromodulation,acousto electrophysiological neuroimaging,closed loop brain stimulation,deep reinforcement learning,epilepsy,computational modeling