Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : Fingers4Cure (Horizon 2020, appel 2014, Proof of concept grants)
Entité participante : Imperial College London — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 149995.0 €

Huntington’s disease (HD) is a lethal inherited neurodegenerative disorder which currently has no cure. Patients develop a devastating loss of muscle control and brain function, with symptoms typically developing at 35 - 45 years of age, although they can start much earlier. Suffering is high for both patients and carers, with death generally occurring within 10 - 15 years of diagnosis. We are working on a novel, curative gene therapy approach based on reducing the levels of the HTT disease gene products, while maintaining the levels of the normal protein. It is based on the delivery of proprietary zinc finger proteins (ZFPs) to the brain. Efficient delivery is accomplished through the use of viral vectors that carry the sequences coding for our ZFPs. Such ZFPs are artificially engineered proteins that specifically bind the defective expanded HTT DNA sequences (but not the normal HTT sequences or other, unrelated sequences). Binding represses the synthesis of the toxic gene products. We have published data demonstrating efficient mutant HTT repression in mouse (Garriga-Canut, M., et al. & Isalan M. Synthetic zinc finger repressors reduce mutant Huntingtin expression in the brain of R6/2 mice. Proc Natl Acad Sci USA 109: E3136-E3145, 2012). We now need to improve the viral vectors for the long term expression of our ZFPs in the mouse brain, as the basis for a single-intervention, long-term therapy in humans (technical improvements). In parallel, we need to maintain the patent applications that protect the technology through various national and regional phases, and protect new developments that may arise (intellectual property), while engaging the necessary IPR and market studies/activities that help us find an industry partner that licenses our technology and moves it forward into the clinic (exploitation). This proof of concept grant is critical in all three aspects.

Mots-clés : zinc fingers,protein engineering,gene therapy
Projet ERC : Commander (Horizon Europe, appel 2023, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe Sciences
Entité participante : Université d'Oslo — Pays : Norvège — Rôle : PI
Acronyme de l'entité : UiO
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2281249.0 €

Detecting inflationary gravitational waves from the Big Bang and mapping cosmic structure formation rank among the most important goals in modern cosmology, and detailed cosmic microwave background (CMB) measurements is a uniquely powerful probe of these effects. However, the predicted signatures are tiny, and their detections require unprecedented instrumental sensitivity and systematics control. In this project I propose to develop one single massively parallel end-to-end framework for the joint analysis of past, present and future CMB experiments, and use this to combine current data from WMAP, Planck LFI+HFI and others with forthcoming measurements from Simons Observatory (SO), all processed at the level of time ordered data (TOD). I will also prepare for the analysis of future CMB experiments, including LiteBIRD, CMB-S4, and a Voyage 2050 CMB spectral distortion probe. This framework will build on an Open Source Bayesian CMB Gibbs sampler called Commander that has already played a transformational role in the field for more than two decades, and that has recently been used successfully to derive new state-of-the-art frequency maps for both Planck LFI and WMAP. However, the existing code only scales well up to O(100) computing cores, and I propose in this project a new organization that will scale it up to O(100,000) cores, as required for next-generation experiments. I will also implement a wide range of ground-breaking TOD-level corrections for key systematic effects (non-linear ADCs, cosmic ray glitches, atmospheric fluctuations, detector cross-correlations etc.) that optimally exploit synergies between experiments. Once operational, I will use this global framework to establish a new state-of-the-art model of the microwave sky; shed new light on several hotly debated LCDM tensions; and, perhaps, make the world's first detection of primordial gravitational waves. This work represents a paradigm shift in the field of computational cosmology.

Mots-clés : cosmic microwave background,bayesian methods
Projet ERC : GLOBAL (Horizon Europe, appel 2022, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : Institut universitaire européen de Florence — Pays : Italie — Rôle : PI
Acronyme de l'entité : EUI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2499931.0 €

This project investigates the dimensionality of global politics. It analyses the cleavages that structure world politics and asks whether conflicts are shaped territorially or along functional dimensions cutting across world regions. The question of whether individuals worldwide are opposed territorially or according to socio-economic and cultural groups is addressed in a long-term empirical analysis spanning historical phases from the 19th century to the present. The goal is to establish if, and under what conditions, international divisions opposing world regions – core−periphery, North−South, or civilizational contrasts –increasingly blur giving way to conflict lines that oppose groups functionally, such as class or education. This is done at the level of citizens, actors and institutions. GLOBAL combines a comparative approach (to detect convergence between world regions) and supranational one (to detect functional dimensionality in global discourse and institutions). The project categorizes cleavages according to three types of inequality: socioeconomic, political-military and cultural. Unlike economic work, it focuses on the politics of inequality, i.e. how actors compete to politicize different inequalities, with narratives legitimizing a more or less equitable redistribution of resources to correct for inequalities. A multi-pronged research strategy based on statistical, scaling, GIS and network methods is used to analyse electoral, socio-economic, roll-call, text, survey and organizational data. GLOBAL will be led by Daniele Caramani and hosted at the European Governance and Politics Programme (European University Institute), of which he is the director. It will lead to an authored book by the PI that follows – as third instalment with common theoretical foundations – the volumes on the nationalization and Europeanization of politics (Cambridge UP, 2004, 2015), as well as a special issue, an edited volume and 11 journal articles by the researq

Mots-clés : world politics,political cleavages,global territoriality,class inequality,left right,core periphery,civilizational divides,quantitative longitudinal analysis
Projet ERC : DARK-ROOTS (Horizon Europe, appel 2023, Advanced grants)
Domaine scientifique : Life Science
Panel : Environmental B iology, Ecology  and E volution
Entité participante : Wageningen University & Research — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : WUR
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2500000.0 €

The origin of the eukaryotic cell, with its complex and compartmentalized features, represents a prime hallmark in the evolution of life on Earth. Yet, important details underpinning the emergence of cellular complexity of eukaryotic cells remain thus far unresolved. Current views support evolutionary scenarios in which the first eukaryotic evolved via a merger between an archaeal host cell and one or more bacterial endosymbionts. Recent phylogenomic work from my lab has shown that the archaeal host cell evolved from within the Asgard archaea, an archaeal clade uniquely displaying several eukaryote-like features. However, key aspects regarding the nature and biology of the archaeal host cell remain elusive, limiting our current understanding of the early stages of eukaryogenesis. In DARK-ROOTS, my team will capitalize upon our unique position and foundational results, and use complementary approaches to elucidate the emergence of complex cellular features during eukaryogenesis. Firstly, building on encouraging preliminary results, we will use an advanced anaerobic cultivation infrastructure to enrich diverse Asgard archaeal lineages. Next, we will use high-resolution and live microscopy approaches to study their cellular ultrastructures and cell biological properties. Furthermore, we will monetize on recent breakthroughs in structural biology and use AI-guided structural genomics to uncover new eukaryotic signature proteins in Asgard archaea. Finally, we will trace the evolution of Asgard archaeal proteins, focusing on homologs of proteins that give stature to the complex and compartmentalized nature in eukaryotic cells. Altogether, building on a rich foundation of preliminary results, my group will bring new pieces of the enigmatic eukaryogenesis puzzle to the table by elucidating how intracellular complexity arose during the prokaryote-to-eukaryote transition.

Mots-clés : eukaryogenesis,origin of the eukaryotic cell,archaea,genome evolution,microbial evolution,anaerobic cultivation,microbial cell biology,high resolution imaging,phylogenomics,ancestral reconstruction,structural genomics
Projet ERC : MetaChange (Horizon Europe, appel 2022, Advanced grants)
Domaine scientifique : Life Science
Panel : Environmental B iology, Ecology  and E volution
Entité participante : Martin Luther University Halle-Wittenberg — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : MLU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2314071.0 €

Despite connections between metacommunity level processes that maintain biodiversity and anthropogenic pressures that erode those processes, these have not been well integrated. I propose a multifaceted approach that (1) builds on metacommunity theory to develop expectations for the influence of anthropogenic drivers on scale-explicit biodiversity change; (2) uses this theory to develop an analytical pipeline to disentangle local and landscape factors driving this change; (3) use the derived results to develop scale-explicit projections of future biodiversity change that can aide biodiversity policy decisions.  With theoretical expectations and analytical procedures in hand, we will synthesize biodiversity change across scales, and the relative influence of local versus landscape-level drivers on those changes. We will apply our analytical pipeline to the largest compilation of metacommunity time series to provide a comprehensive analysis of biodiversity change. We will complement this with a data collection campaign to ‘resurvey’ zooplankton metacommunities from ponds that had been surveyed 10-50 years previously, but which have experienced different levels of background ‘change’ (e.g., loss of nearby habitat). We will also compile a database to explore the potential influence of habitat restoration via local and landscape processes, and its influence on scale-explicit biodiversity change. We will analyse these synthetic datasets together with geospatial driver data using our novel analytical pipeline. Finally, we will develop a unique pipeline integrating metacomunity theory with our synthetic results and machine learning to develop projections of biodiversity change in the face of scenarios of anthropogenic change. With the information gained, we will contribute not only to a better understanding of the most important feature of life on this planet—its biodiversity—but by developing this understanding, we can provide an avenue to mitigate the changes.

Mots-clés : metacommunity theory,synthesis,restoration,land use,habitat loss,dispersal limitation,resurvey,pond,wetland,forest,grassland
Projet ERC : GLACMASS (Horizon Europe, appel 2022, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth System Science
Entité participante : Université d'Oslo — Pays : Norvège — Rôle : PI
Acronyme de l'entité : UiO
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2499957.0 €

World-wide glaciers are losing mass which affects global sea-level, river runoff, freshwater influx to the oceans, glacier-related hazards, and landscape changes, with implications for human livelihoods and ecosystems. Hence, accurate estimates of past, current and future glacier mass variations at a high temporal and spatial resolution are key to effective adaptation strategies. However, previous mass-balance reconstructions and projections have relied on scarce observations with limited spatial and/or temporal resolution, as well as overparameterized, insufficiently constrained and highly simplified models, the latter necessitated by high computational costs incurred by the global scale.  GLACMASS will propel the current state-of-the-art of global-scale glacier reconstruction and projection forward in unprecedented ways by delivering a fundamentally novel and internally consistent physically-based modelling framework that draws, for the first time on a global scale, on both data assimilation and modern machine learning techniques facilitated by emerging global-scale glacier-related satellite-derived data. The framework will be used to reconstruct multi-decadal past glacier changes, and make policy-relevant multi-century projections of mass and area changes of all >200,000 glaciers outside the ice sheets with unprecedented accuracy, spatiotemporal detail and computational efficiency, and also nowcast present mass changes in a near-real-time fashion for selected regions. The model framework will fuse output from a novel physically-based glacier evolution model with all relevant observations available for each glacier, such as in-situ, geodetic and gravimetry-derived mass balances, as well as snowlines and other observations, thus simultaneously exploiting the untapped strengths of different types of observational data sets in an optimal manner.

Mots-clés : glaciers,glacier mass balance,global glacier modelling,glacier projection,glacier evolution,data assimilation
Projet ERC : REMINDS (Horizon Europe, appel 2021, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Materials Engineering
Entité participante : Université autonome de Barcelone — Pays : Espagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2499940.0 €

With the advent of Big Data, information is facing new, potentially more damaging, security threats. The current trend to enhance data protection is to use increasingly complex mathematical algorithms to encrypt information. This approach requires exponentially growing amounts of data, time and power resources. REMINDS proposes a radically new concept to boost data security: to act directly at material level, i.e., in the way information is stored. The project is built on the disruptive idea of using voltage to activate/deactivate magnetism via strain or ion migration effects and it tackles novel strategies to control the mutual interactions between ferromagnetic (FM), antiferromagnetic (AFM) and ferroelectric (FE) materials. While data written in ferromagnets can be read using conventional heads, AFM and FE materials are ‘invisible’ to magnetic sensors due to their lack of magnetic stray fields (methods to read sub-200 nm AFM or FE domains are complex and often destructive). REMINDS will develop advanced engineering protocols to (i) transfer information from FM to AFM or FE materials, (ii) keep the data ‘hidden’ in the AFM or FE layers while the FM state is switched off and (iii) retrieve the information whenever deemed necessary. Neuromorphic-inspired layouts will be used to selectively apply these protocols to specific memory units that will incorporate stochastic physical phenomena. This will be the basis of new energy-efficient proof-of-concept data protection designs whose working principle will be tested at lab scale for potential applications in anti-counterfeiting and anti-hacking technologies. REMINDS is expected to revolutionize magnetoelectricity, exploiting voltage-programmable magnetism to an unprecedented extent and forging an entirely new paradigm in data security. Its outcomes will bring ground-breaking scientific contributions to the fields of magnetism, spintronics, piezotronics and flexible electronics, and will have a huge socio-economic impact.

Mots-clés : magnetoelectric effects,magneto ionics,domain imprinting,exchange bias,heterostructured multiferroics,unclonable systems,materials for data security,energy efficiency,crossbar arrays
Projet ERC : CONDJUST (Horizon Europe, appel 2021, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Human Mobility, Environment, and Space
Entité participante : Université autonome de Barcelone — Pays : Espagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2491924.0 €

CONDJUST will create a new research field, Conservation Data Justice, that bridges three distinct areas of enquiry: conservation prioritisation, political ecology and Data Justice. The former uses data which risk marginalising rural peoples. The latter does not yet examine conservation data. Meanwhile political ecologists do not yet consider Data Justice approaches when tackling conservation prioritisation. CONDJUST will interrogate conservation data and models, and explore the epistemic communities producing them, to develop new theories of socially just, data-driven conservation. It will challenge the colonising tendencies of prioritisation work and seek decolonising alternatives.  CONDJUST is timely because ambitious new global targets seek to safeguard 30% of the planet for conservation by 2030 (and more afterwards). These plans pose risks for rural people because the data and modelling they use can contain diverse forms of bias, exclusion and omission. These risks will grow as more social media data are used in conservation prioritisation. We need insights from Data Justice to understand these dangers, and how they might be counter-acted.  This project has four objectives, each with a corresponding work package. These are:  1. Systematically examine the sources of bias and distortion in conservation data used in global prioritisation work. 2. Use Data Justice thinking in new analyses of biodiversity conservation, and increase our understanding of socially just conservation prioritisation. 3. Critically explore the construction of different epistemic communities in conservation prioritisation, and political ecology, to understand what inhibits and enhances learning between them. 4. Examine how policies responding to prioritisation are shaped by, or resist, the new measures proposed.  These work packages will be pursued by an interdisciplinary team led by the PI and composed of three post-doctoral researchers, two PhDs, an administrator and an advisory board.

Mots-clés : political ecology of conservation conservation social science data justice decolonisation wildlife and biodiversity conservation conservation prioritisation
Projet ERC : FRONTIER (Horizon Europe, appel 2022, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Université technique de Prague — Pays : Rép. Tchèque — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2499825.0 €

Computer vision is beginning to see a paradigm shift with large-scale foundational models that demonstrate impressive results on a wide range of recognition tasks. While achieving impressive results, these models learn only static 2D image representations based on observed correlations between still images and natural language. However, our world is three-dimensional, full of dynamic events and causal interactions. We argue that the next scientific challenge is to invent foundational models for embodied perception – that is perception for systems that have a physical body, operate in a dynamic 3D world and interact with the surrounding environment.  The FRONTIER proposal addresses this challenge by means of: 1. developing a new class of foundational model architectures grounded in the geometrical and physical structure of the world that seamlessly combine large-scale neural networks with learnable differentiable physical simulation components to achieve generalization across tasks, situations and environments; 2. designing new learning algorithms that incorporate the physical and geometric structure as constraints on the learning process to achieve new levels of data efficiency with the aim of bringing intelligent systems closer to humans who can often learn from only a few available examples; 3. developing new federated learning methods that will allow sharing and accumulating learning experiences across different embodied systems thereby achieving new levels of scale, accuracy, and robustness not achievable by learning in any individual system alone.  Breakthrough progress on these problems would have profound implications on our everyday lives as well as science and commerce with safer cars that learn from each other, intelligent production lines that collaboratively adapt to new workflows or a new generation of smart assistive robots that automatically learn new skills from the Internet and each other enabled by the advances from this project.

Mots-clés : embodied computer vision,self supervised learning,federated learning
Projet ERC : GT4Pebbles (Horizon Europe, appel 2024, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe Sciences
Entité participante : Université d'Amsterdam — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : UvA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2013922.0 €

The discovery of exoplanetary systems has triggered a boost in the development of planet formation models. Most of these models rely on the presence of so-called pebbles – dust aggregates that have grown and been compacted in just the right way to be dynamically loosely coupled to the gas in the planet-forming disk. Only then do they enable key processes that produce planetary seeds and grow them efficiently into planets, overcoming growth barriers and enormous time scale problems. The models require that conversion of micron-sized dust particles into pebbles is global, efficient and fast. However, this is currently an unproven assumption that renders base assumptions of these models as shaky. GT4Pebbles will answer the following questions:  - Do pebbles with useful properties for planet formation actually form, and with sufficient abundance? - What are the quantitative properties of these pebbles, and the consequences for planet formation models, disk observations and disk mass measurements?  Building upon my recent advances in modeling and disk observations, and on advances in experimental technology in our partner laboratory, we will develop the first full-size range fractal dust-agglomeration experiment. We will gain comprehensive insights into the formation and properties of dust aggregates without relying on extreme extrapolations. By integrating experimental results with advanced numerical models, we will derive the structural, mechanical and optical properties of these aggregates as they evolve into pebbles. We will create a robust global dust growth model that for the first time meticulously accounts for porosity and compaction phenomena. This research will enable us to identify and characterize the initial seeds crucial for planet formation models in disks, providing valuable contributions to our understanding of planetary-system formation.  With GT4Pebbles, planet formation models will no longer be “built on sand”, but on a solid foundation.

Mots-clés : laboratory astrophysics,numerical methods,optical properties