Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : ASYKNOW (Horizon Europe, appel 2022, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Human Mobility, Environment, and Space
Entité participante : Université de Bergen — Pays : Norvège — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Migration, and particularly the question of asylum, has become one of the most pressing and polarizing issues of our time. Scholars have highlighted how the high level of contestation and politicization in this area has increased the demand for a wide range of expert knowledge. At the same time, this situation has thrown expert knowledge into a terrain of conflict. Consequently, the notion that expert knowledge provides a proper foundation for legal decision-making regarding asylum is under pressure. ASYKNOW will develop new conceptual tools for understanding the role of expert knowledge in asylum governance by investigating the ways in which knowledge about asylum seekers and migration is constituted and contested through asylum litigation. The project moves beyond traditional approaches to expert knowledge in migration scholarship, which has largely examined the content of decisions and adopted a one-directional approach to expert knowledge as an instrumental and/or symbolic tool of states’ domination. Instead, a focus on asylum litigation allows us to examine 1) how knowledge claims attain meaning and authority through the relational dynamics between differently situated actors, 2) how legal thresholds and tactics evolve as they pertain to the use of expert knowledge, and 3) how types of knowledges and knowledge claims facilitate or challenge states’ ability to exercise power over bodies and spaces. ASYKNOW brings together theoretical concepts, methodological approaches, and findings from geography, anthropology, law, and Science and Technology Studies (STS) – that have hitherto remained fairly separate with regard to the study of migration – into a novel and productive dialogue. Methodologically, the project adopts an ambitious in-depth comparative research design that innovatively combines an ethnography of legal processes with legal archaeology. Empirically, the research will focus on litigation of asylum-related cases in Norway, Sweden, Denmark, and Germany.

Mots-clés : expert knowledge,asylum litigation,law,ethnography,legal archeology,geography,anthropology,science and technology studies
Projet ERC : SHADES (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Studies of Cultures and Arts
Entité participante : Université de Florence — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1250000.0 €

Colour in art is more than mere decoration; it is a vital element of human expression and cultural heritage. Polychrome artworks offer profound insights into historical practices, societal values, and technological advancements across different cultures. Yet, these colours are highly susceptible to deterioration, particularly when exposed to light. Photo-degradation is a complex process and remains unpredictable today, causing irreversible changes that result in fading and alteration of hues. This not only compromises the aesthetic and material integrity of the artefacts but also impedes our ability to fully comprehend the artist’s original vision and poses significant challenges for preserving these invaluable treasures. SHADES introduces an innovative method for detecting and predicting colour fading before it becomes visible to the naked eye. This approach utilises Fluorescence Lifetime Imaging Microscopy (FLIM) to capture lifetime signal shifts, allowing us to address the inherent unpredictability of photo-degradation. By analysing mock-ups created with traditional recipes for common polychrome layers (binder, dye, and varnish) and subjecting them to controlled exposure to mono- and polychromatic light, we aim to: i) identify the unique 'fingerprint' of each material; ii) monitor signal shifts indicative of photo-degradation; and iii) develop a predictive model to detect colour changes before they are perceptible. By integrating various disciplines, SHADES is set to revolutionise the field of material degradation and conservation. The project will yield accurate predictive models and advanced techniques for preserving polychrome artworks, addressing colour fading before it becomes visible. Its impact extends beyond conservation and restoration, influencing museum exhibitions where light management is critical and advancing material science, ensuring the enduring vibrancy and historical integrity of these priceless artworks.

Mots-clés : polychrome artworks fluorescence lifetime colour fading heritage science conservation science organic dyes organic binders
Projet ERC : 3DnanoGiant (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Materials Engineering
Entité participante : Université de Florence — Pays : Italie — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 197500.0 €

Photonics has a major impact on technological innovations and can revolutionize the field of computing and data processing due to its high bandwidth, speed, and low power consumption. This has already happened for data communication via fiber-optic connections, while light processing in embedded systems is still performed by electronics via power-hungry optical-electrical conversion.  The future development of integrated photonics is then waiting for new integrable materials that can advance the functionality of photonic chips towards all-optical signal processing at very low light intensities. While linear operations and parallel matrix multiplication can be efficiently performed by light, nonlinear optical functions are notoriously difficult to implement because they require a suitable medium for efficient photon-photon interaction. Currently, there is a lack of materials with large third-order nonlinearity, high speed and easy processability and integration in 2D and 3D structures.  3DnanoGiant aims to develop new nonlinear photonic materials that can be integrated into heterogeneous functional platforms or chiplets. To this end, I will exploit the giant optical nonlinearity of liquid crystals (ten orders of magnitude larger than that of silicon), for new formulations and lithographic strategies by which the liquid crystals will be confined in a printable nano-porous polymer network. Their 3D nanopatterning will enable the production of multidimensional hybrid nonlinear photonic devices, from all-optical 2D logic gates and ultrafast nonlinear activation functions to self-oscillating 3D photonic crystals. In parallel, the propagation, interaction and polymerization of solitons in a 3D(+1) space will lay the foundation for a new unsupervised bottom-up 3D printing technology. The goals of 3DnanoGiant will redefine the state-of-the-art in integrated nonlinear photonics with practical and versatile heterogeneous chip for fast and energy-efficient optical processing.
Projet ERC : IMR (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Soci al World and Its Interactions
Entité participante : Université de technologie de Lappeenranta-Lahti — Pays : Finlande — Rôle : PI
Acronyme de l'entité : LUT University
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499058.0 €

Along with artificial general intelligence (AGI), the development of brain-computer interfaces (BCI) is heralded as the new frontier for the data-driven economy. It stands at the forefront of an arms race involving the world’s most valuable companies and powerful states. However, social science scholarship on neurotechnology has largely overlooked core socioeconomic aspects of non-medical applications of BCI, such as their impact on productivity, labour processes, business models, and data extraction schemes. Socioeconomics and critical data studies, on the other hand, have not yet addressed BCI. This research innovates by approaching neurotechnology as a new frontier for data accumulation and exploring how its application in training and work environments connects to a broader societal change wrought by the digital economy: the crisis of the human capital paradigm. It proposes that the demise of human capital is giving way to an emerging alternative paradigm for governing the workforce, defining it as the ‘Intelligence Management Regime (IMR)’ – a new (datafied) ecology of competencies and practices driven by explorations in neuro-enhancement and novel complementarities between humans and machines. At the crux of these developments is a thorough revision of the socially dominant definition of (human) intelligence, which our research will explore. IMR will employ documentary and fieldwork research with major global computational neuroscientific projects, leading neurotech companies, as well as businesses, consumers, and workers using these artefacts in daily activities. In a globalised economy, the investigation requires a comparison of distinct geoeconomic realities where neurotech diffusion is more advanced: the US, Europe, and China. By advancing a new understanding of what are deemed subjective and objective dimensions in human interactions with technical objects in labour settings, the project prompts path-changing conceptual developments in social theory.

Mots-clés : bci neurotechnology labour intelligence human capital brain,datafication governmentality
Projet ERC : LASU (Horizon Europe, appel 2025, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Texts and Concepts
Entité participante : Université de Florence — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499803.0 €

Sumerian is probably the earliest recorded language of humankind and its writings are among the most significant sources for studying the ancient Near East. It has been documented in the cuneiform script for over three millennia, from approximately 3300/3200 BCE to 74/5 CE. To date, however, a fundamental question in the field remains elusive: Why, if the last native Sumerian speaker passed away no later than the beginning of the second millennium BCE, did Sumerian continue to be used by the cultural and religious elite until the beginning of the Common Era? What was it about Sumerian that enabled it to trump Akkadian, Aramaic, Persian, Parthian, and many other languages of that time in the lottery of longevity? The main hypothesis of LASU is that Sumerian survived for so long because it was considered sacred. Two interrelated obstacles have prevented scholarship from advancing in this area of knowledge. First, Sumerian texts of the first millennium BCE, referred to as Late Sumerian, were often written in a bilingual for-mat, combining Sumerian and Akkadian, and are extremely challenging to translate. Second, modern scholars often consider this phase of Sumerian “corrupted” in comparison to the earlier stages, when it was likely still a spoken language. They therefore have tended to overlook Late Sumerian. Applying, for the first time, an innovative philological and religious-historical approach to tackle the longstanding puzzle of Sumerian linguistic longevity, LASU aims to apply a unique, language-order-based framework for deciphering Sumerian’s interactions with other languages of the multilingual ancient Near East of the first millennium BCE. The proposed study has the potential to open new vistas on the interactions among ancient languages, as well as lay the foundation for a ground-breaking understanding of how ancient societies dealt with multilingualism. Ultimately LASU is poised to establish a new subfield called “Late Sumerian Studies” within Assyriology.

Mots-clés : sumerian,first millennium bce,bilingualism,literature,magic,religion
Projet ERC : LOOPS (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Soci al World and Its Interactions
Entité participante : Université libre de Berlin — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499605.0 €

Imagine you protest the slide of your country into an autocratic regime. The weather is freezing and you rally in these conditions for weeks. Not a pleasant idea, yet in 2004 thousands of Ukrainians demonstrated against electoral fraud and established a permanent protest camp in Kyiv. For two months, thousands of protesters were in this urban camp, requiring food, toilet facilities and accommodations to regain warmth. And not only in Ukraine, but all over the world, people protest under difficult conditions and have to overcome logistical and personal challenges to pursue their goals. While the literature on social movements and revolution has engaged extensively with popular uprisings and large-scale protests, the logistics that are required for these protests to succeed under autocracy are hardly ever addressed. Existing research has long neglected the fundamental questions that LOOPS engages with. Such as: what logistical activity can be observed during these protests, their quantity and professionalism? Who organizes the portable toilets, provides soup, and blankets? And what effect has the level of professionalism on the legitimacy of the protest and autocratic stability? We will first develop a concept of professionalism in backstage logistics and a theoretical argument about its effect on the legitimacy and course of protests. With this we will create a unique dataset of the logistics during uprisings based on the analysis of images, a new and exciting way to generate data. With a substantive human coding effort, LOOPS will identify the basic logistics, their type, quantity, and professionalism. In a second step, LOOPS will include case studies to gather more information about financial and organizational sources for protest logistics. Third, we will test the impact of these logistics on autocratic resilience. I propose an ambitious research agenda, address a salient research gap, offer an innovative approach to data collection and a multi-method design.
Projet ERC : NoOne (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and Processes Engineering
Entité participante : Académie d'Åbo — Pays : Finlande — Rôle : Partner
Acronyme de l'entité : Åbo Akademi University
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 530566.25 €

Biomarkers are measurable indicators of a particular disease state of an organism. There has been an increasing demand for diagnostic markers, enabling reliable and non-invasive screening of peripheral biofluids. It is also established that a universal screening against life-threatening diseases, such as cancer, or infectious disease outbreaks, can be accomplished only by combining genomic and protein marker-based tests. The NoOne project aims at conceiving, engineering, fabricating and validating a ground-breaking platform based on a single-molecule binary bioelectronic sensor, capable to reliably discriminate biofluid samples enclosing zero biomarkers from those containing just one. The technology can be used for ultimate binary sensing of both proteins/peptides and genomic markers to enable the reliable screening of diseases such as cancer as well as viral and bacterial infections. The NoOne binary platform is designed to be portable, cost-effective, easy to operate and with a time-to-results within one hour; hence it is the ideal candidate for point-of-care applications. The prototype will enable clinicians, phytopathologists or veterinaries, to identify the set of samples that are totally free from a protein, peptide or genomic marker as well as from a pathogen (virus or bacteria), from those enclosing at least one with a confidence level of 99%. This makes the NoOne platform the best performing ever in enabling a fast, highly reliable, cost-effective identification of the subset of biological samples belonging to the potentially diseased part of a population. This is of paramount importance for predictive screening of humans, plants or animals. NoOne will demonstrate its effectiveness in key relevant applications such as the binary detection of pancreatic cancer biomarkers, SARS-CoV-2 virus, the Xylella Fastidiosa bacterium and the assay of post translational peptides evidencing the phosphorylated forms regulating crosstalk with oncogenic signalling pathways.

Mots-clés : biosensors,organic bioelectronics,analytical chemistry,biological markers screening,organic filed effect transistors
Projet ERC : ExCEL (Horizon Europe, appel 2023, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Université de Murcie — Pays : Espagne — Rôle : PI
Acronyme de l'entité : UM
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499995.0 €

The manufacture of many high-value chemicals that sustain our daily lives depends on the ability of palladium catalysts to link together (cross-coupling) complex structural motifs. Yet, in view of the rapid increase of the price of palladium and its progressive depletion, it is crucial to invent alternative and more sustainable systems based on Earth-abundant metals to ensure the viability in the long term of these strategic processes that provide us with materials, agrochemicals or medicines. Iron is considered the most benign of transition metals because it is endless, inexpensive and biocompatible. However, despite its early discovery, iron-catalyzed cross-couplings have been underutilized owing to their narrower scope and the need for strong organometallic reductants or harsh conditions, which hampers their applicability in complex targets and densely-functionalized substrates. This proposal introduces a fundamentally new approach to overcome the issues that restrain the development of iron-catalyzed cross-couplings, exploiting the innate ability of iron complexes to harvest light and repurposing it to “activate” catalysis. Capitalizing on novel modes of reactivity accessed upon visible-light irradiation, this research programme offers a strategy to access key catalytically active iron species under mild conditions without the use of strong organometallic reductants, enabling unprecedented transformations with extended scope. ExCEL recruits charge-transfer excited states of iron complexes, providing access to Fe(I)/Fe(III) and Fe(0)/Fe(II) catalytic manifolds to achieve C–C and C–heteroatom bond formation as well as multicomponent reactions that are currently out of the reach of state-of-art iron catalysis. Overall, this proposal aims to introduce a new paradigm to upgrade and unleash the full potential of iron catalysis in organic synthesis, and will pave the way for the discovery of exciting new synthetic transformations.

Mots-clés : photocatalysis,iron catalysis,cross coupling,excited states,photochemistry,sustainability
Projet ERC : beyond2 (Horizon Europe, appel 2024, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : École internationale supérieure d'études avancées — Pays : Italie — Rôle : PI
Acronyme de l'entité : SISSA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499999.0 €

Deep neural networks (DNNs) have revolutionised how we learn from data. Rather than requiring careful engineering and domain knowledge to extract features from raw data, DNNs learn the relevant features for a task automatically from data. In particular, high-order correlations (HOCs) of the data are crucial for both the performance of DNNs and the type of features they learn. However, existing theoretical frameworks cannot capture the impact of HOCs – they either study “lazy” regimes where DNNs do not learn data-specific features, or they rely on the unrealistic assumption of Gaussian inputs devoid of non-trivial HOCs.  beyond2 will develop a theory for how and what neural networks learn from the high-order correlations of their data. We break the problem into two parts: (i) *How?* We analyse the learning dynamics of neural networks trained by stochastic gradient descent to unveil the mechanism by which they learn from HOCs efficiently (in terms of the minimum amount of training data / learning time required to attain satisfactory predictive performance). (ii) *What?* Our preliminary research suggests that neural filters are primarily determined by the “principal components” of HOCs. We investigate how these principal components relate to fundamental data properties, such as symmetries of the inputs.  We attack these problems by extending methods from statistical physics and high-dimensional statistics to handle non-Gaussian input distributions. Studying the interplay between data structure and learning dynamics will allow understanding how specific learning mechanisms, like attention or recursion, are able to unwrap HOCs.  By shifting the focus from unstructured to non-Gaussian data models, beyond2 will yield new insights into the inner workings of neural networks. These insights will bring theory closer to practice and might facilitate the safe deployment of neural networks in high-stakes applications.

Mots-clés : theory of neural networks statistical physics high dimensional statistics
Projet ERC : QARA (Horizon Europe, appel 2021, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental Constituents of Matter
Entité participante : Université d'Innsbruck — Pays : Autriche — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 695525.0 €

Rydberg atoms arrays have emerged as a promising platform for assembling quantum matter in a bottom-up approach. This platform combines deterministically prepared, reconfigurable arrays of individually trapped cold atoms with strong, coherent interactions enabled by excitation to atomic Rydberg states and in situ state readout. It enables a scalable realization of quantum spin models with system sizes beyond what can be simulated on classical computers. The main goal of this project is to harness many-body properties of Rydberg atoms for applications in next-generation quantum technology. The first goal is to explore the possibility to harness a novel, robust dynamical quantum many-body phenomenon as a tool to generate useful entangled states in systems with limited control. Specifically, we will study the entanglement properties of quantum many-body scars, a natural phenomenon first discovered in Rydberg atoms arrays, regarding their utility for measurement-based quantum computation, quantum error correction and quantum metrology. Our second goal is to study and develop novel approaches for implementing information processing protocols with Rydberg atom arrays, based on an intimate connection to maximum independent set problems. This includes the exploration of analog quantum annealing algorithms, the development of noise-resilient gate sets for circuit-based approaches, and preparation and manipulation of topologically protected qubits. The third objective is to develop novel tools to characterize quantum many-body states of Rydberg atom arrays and access fundamental properties, such as entanglement measures.