Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : QUINTESSEnCE (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental Constituents of Matter
Entité participante : Université de Florence — Pays : Italie — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 151820.0 €

Isolating and addressing individual quantum systems has allowed for breakthrough results in quantum mechanics. Today, increasing the complexity of the system while maintaining control at the single-quantum level is vital for the next generation of quantum devices and research. QUINTESSEnCE will take up this challenge by developing interfaces between single photons, spins and phonons, all within one simple physical system, i.e. a single molecule. Fundamental systems like molecules have the inherent advantage, in comparison to artificial structures, of being nominally identical. A molecule can have the coherence properties of an atom even when embedded in a solid, without losing the access and customization opportunities typical instead of the solid state. Molecules differ from atoms in being more complex systems, with rich energy diagrams structured over multiple scales. We propose to leverage this complexity to coherently connect optical frequency photons with microwave spin excitations and gigahertz phonons. Unprecedented control over the molecules’ degrees of freedom will be achieved by integrating them in nanostructured devices. We will develop a ground-breaking lab-in-a-molecule platform, benefiting from the tunability and scalability of molecules, so as to aim at the following main objectives: • Complex states of light: integrating multiple molecular sources of indistinguishable photons on chip • Single-molecule cavity optomechanics: accessing the regime of single-photon strong coupling in an unconventional cavity optomechanical system • Optical addressing of single molecular spins: providing a crucial knob to read out and control the spin state of a single molecule QUINTESSEnCE will therefore allow us to entering unexplored quantum territories and to develop quantum-technology tools unavailable today. Notably, the outcome of this project will impact a broad scientific community, touching quantum optics, optomechanics and molecular quantum technologies.

Mots-clés : single molecule physics quantum emitters interfaces hybrid systems
Projet ERC : PALEOCORE (Horizon Europe, appel 2023, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth System Science
Entité participante : Université de Lund — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999854.0 €

PALEOCORE will provide the first comprehensive observational constraints on the dynamics of Earth’s core on multi-centennial to millennial timescales. Such constraints are essential to understand the core processes responsible for the rapid decay of Earth’s dipole field strength over the past century and to forecast future field changes.  Generated through convective motions in the liquid iron core, Earth’s magnetic field acts as a shield against harmful cosmic radiation and plays a crucial role for the habitability of our planet. The past two decades of satellite monitoring of the magnetic field, in combination with major advancements in numerical simulations of the geodynamo, have generated a wealth of knowledge on relatively rapid processes in the core. However, due to the lack of reference data with adequate resolution, the dynamics of the core on timescales longer than the convective overturn time (~130 years) are still poorly understood. Observational constraints of core dynamics on these timescales are crucial to evaluate proposed driving mechanisms of the geodynamo.  Through recent technical innovations, models based on indirect paleomagnetic observations of Earth’s magnetic field are providing information on past field changes with unprecedented resolution. These models suggest that the recent dipole decay is part of a millennial-scale recurrent pattern associated with weak field anomalies, like the present-day South Atlantic Anomaly. The aim of PALEOCORE is to construct the first ever integrated core-field core-flow model over millennial timescales to study such ancient analogues and reveal the underlying core dynamics responsible for driving these changes. This will be achieved through a combination of (i) strategic paleomagnetic data acquisition and key modelling innovations (solving bottlenecks in the current approach), (ii) incorporation of independent radionuclide data and (iii) adaptation of data assimilation algorithms for paleomagnetic data.

Mots-clés : geomagnetism,paleomagnetism,geodynamo,cosmogenic radionuclides
Projet ERC : HotLife (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Life Science
Panel : Environmental B iology, Ecology  and E volution
Entité participante : Université de Lund — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999966.0 €

Global warming and heatwaves – hallmarks of climate change that are expected to worsen in the 21st century – diminish reproductive success and cause excess mortality in wild and domesticated animal populations. Yet, we still have sub-standard knowledge of almost all fundamental traits that medi-ate success or failure under climate change, and, for most animals, there are no data on the extent to which plasticity, local adaptation, or evolutionary change can alleviate the stress imposed by a hotter world. Collectively, therefore, the research field is at status quo. To mitigate this situation, HotLife will leverage my previous phenomenological studies on ontogenetic priming and plasticity of thermoregulatory capacity to generate novel knowledge on when and why animals fail in the heat, and whether plastic or evolutionary responses may render failure a less likely outcome in the future. The main outcomes of HotLife will reveal: 1) How heat tolerance manifests, how much it can evolve, and whether plasticity halts evolutionary rescue; 2) Whether climate warming and climatic extremes directly or indirectly cuts life expectancy short; and 3) If the physiological basis for heat tolerance is inherited in the wild and if it is already being selected upon  I will achieve these ambitious goals by combining state-of-the-art thermal and sub-cellular physiology with intricate genomic techniques and powerful heritability analyses in captive and wild bird models – the endotherm group for which heat-induced mass mortality is most frequently reported. HotLife goes beyond the state-of-the-art, by i) bridging crucial knowledge gaps between heat failure in birds, its physiological basis, and its links to fitness and survival; ii) addressing the potential for rescue by plasticity or evolution from these effects; and iii) uncovering evidence for local adaptation and heritability of heat tolerance in the wild.

Mots-clés : bird,cross fostering,evaporative cooling,evolutionary change,extreme weather,global warming,heat stress,heat dissipation,heat shock protein,local adaptation,metabolic rate,mitochondria,population decline,thermoregulation,transgenerational plasticity
Projet ERC : EvolvingCircuits (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Université de Lund — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999119.0 €

Of the 1.2 million animal species described on Earth, more than 80% are insects. As for mammals, each insect is equipped with a brain that has evolved to optimally control the species’ behavior in the context of its environment. In more than 450 million years of evolution the neural circuits guiding behavioral decisions have diverged from an ancestral version to enable insects to conquer every terrestrial habitat on the planet and to equip many species with behavioral strategies that rival even mammals in complexity. These circuits thus have to be rigid enough to maintain their ancestral, core functions, while also being sufficiently adaptable to enable the addition of novel functions. The mechanisms of how this is achieved across vast evolutionary timescales are unknown. While this is true for all animals, insects with their numerically simpler brains and a comparably rigid neuroarchitecture offer the unique chance to unravel the evolution of decision making circuits at the level of identified neurons and synapses. Aided by recent technological breakthroughs and the establishment of rich ground-truth data in the fruit fly, I will combine whole brain anatomy, connectomics and computational modeling with electrophysiology and behavior to dissect the evolution of the central decision making center of insect brains, the central complex - across the entire insect phylogeny. I aim to reveal how this region has evolved in the context of the entire brain, how its intrinsic circuits have changed with increasing evolutionary distance (circuit phylogeny), and which changes in its circuitry are linked to specialized behavioral abilities (circuit adaptation). Finally, I will directly establish behavioral correlates of identified circuit features (circuit function), attaching relevance to connectomics data in a way that is achievable only by a wide, comparative approach - raising our understanding of structure-function relations in animal nervous systems to a new level.

Mots-clés : navigation,insects,connectomics,decision making
Projet ERC : CollectiveMinds (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer Science and Informatics
Entité participante : Helmholtz Center for Information Security — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : CISPA
Type d'entité : Recherche
Financement ERC de l'entité : 2000000.0 €

Machine learning models are growing larger and more complex, making training increasingly resource-demanding. Concurrently, our world, and hence the training data is perpetually evolving. This requires continual model updating or retraining to address changing training data. Presently, the most reliable course to handle such distribution shifts is to retrain models from scratch on new training data. This results in substantial resource usage, increased CO2 footprint, elevated energy consumption, and limits the decisive ML progress to large-scale industry players.  Imagine a world in which models help each other learn. When the data distribution changes, a complete retraining of models could be avoided if the new model could learn from the outdated one by using reliable and provably effective methods. Furthermore, the convention of relying on large, versatile monolithic models could then give way to a consortium of smaller specialized models, with each contributing its specific domain knowledge when needed. By encouraging this form of decentralization, we could reduce resource consumption as the individual components can be updated independently of each other.  Drawing on groundbreaking research in distributed ML model training, CollectiveMinds aspires to design adaptable ML models. These models can effectively manage updates in training data and task modifications, while also enabling efficient knowledge exchange across various models, thereby fostering widescale collaborative learning and constructing a sustainable framework for collaborative machine intelligence.  This initiative could revolutionize sectors like healthcare, where there is limited training data, and trustworthy AI that demands guarantees on data ownership and control. Furthermore, it could foster improved collaborative research within the realm of science. CollectiveMinds embodies a significant paradigm shift towards democratizing ML, focusing on cooperative intellectual efforts.

Mots-clés : decentralized machine learning,federated learning,algorithms for ml,optimization for ml
Projet ERC : MICROCHEMIST (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Université d'Édimbourg — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1994806.0 €

The field of engineering biology aims to create new metabolic pathways to access chemical products from sustainable feedstocks via fermentation. However, metabolic chemistry is limited to reactions that have evolved in Nature and remains dwarfed by the chemical diversity accessible from petrochemicals by synthetic organic chemists. Urgent solutions must now be sought to bridge this gap and enable the transition of the chemical industry to a more sustainable, climate-positive and net-zero future. Over the past 8 years I have led the emerging field of biocompatible chemistry, which addresses this challenge by interfacing non-enzymatic reactions with metabolism to expand cellular chemistry. This ambitious research has broken boundaries at the interface of chemical catalysis and metabolic engineering, yet the full transformative potential of biocompatible chemistry has still to be realised, and two key fundamental questions remain unanswered: can biocompatible chemistry be fully integrated with cellular metabolism and what are the rules governing biocompatibility? To answer these questions, MICROCHEMIST and my ERC-CoG team will achieve a step-change in what is possible in this emerging field by interfacing chemo-catalysts with the metabolic function of new microorganisms and within new ambitious metabolic designs. This will open up entirely new directions of travel for biocompatible chemistry by (i) harnessing new metabolic chemistry (ii) interfacing multiple metabolic pathways simultaneously, (iii) uncovering the fundamental rules for biocompatible reaction success, and for the first time (iv) fully integrating non-enzymatic reactions within metabolism to create new metabolic products. This unique and highly multidisciplinary approach will revolutionise the fields of both chemical and biological synthesis and accelerate the transition towards a greener and more sustainable chemical industry.

Mots-clés : chemo enzymatic synthesis sustainable synthesis
Projet ERC : AIMODELS (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Texts and Concepts
Entité participante : Université Ca' Foscari de Venise — Pays : Italie — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1927573.0 €

The project proposes an alternative epistemology of artificial intelligence (AI). It argues that what is at stake in AI is not its similarity to human rationality (anthropomorphism), but its epistemic difference. Rather than speculating in the abstract on whether a machine can “think”, the project addresses a historical question: What is the logical and technical form of the current paradigm of AI, machine learning, and what is its origin? The project traces the origins of machine learning back to the invention of algorithmic modelling (more precisely, algorithmic statistical modelling) that took shape in the artificial neural networks research of the mid 1950s, and records that a coherent history and epistemology of this groundbreaking artefact is still missing. The project pursues three objectives to turn its findings into a constructive paradigm: 1) a new history of AI that stresses the key role of algorithmic models in the evolution of statistics, computer science, artificial neural networks, and machine learning; 2) a new epistemology of AI that engages with the psychology of learning and the historical epistemology of science and technology; 3) a study of the impact of the large multi-purpose models (e.g. Bert, GPT-3, Codex and other recent foundation models) on work automation, data governance, and digital culture. Through consolidating a model theory of AI, the research will benefit the reception of AI in general and fields such as digital humanities, scientific computing, robotics, and AI ethics, among others. Ultimately, it will help situate AI in the global horizon of the current technosphere and in the long history of knowledge systems.
Projet ERC : AiCE (Horizon Europe, appel 2022, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth System Science
Entité participante : Université Ca' Foscari de Venise — Pays : Italie — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 100000.0 €

We are missing a central piece in the puzzle to understanding our Earth’s climate: Its dynamics fundamentally changed during the “Mid-Pleistocene Transition”, when some 1.2 million years ago the oscillation between warm periods and ice ages shifted its periodicity from 41 to 100 ka. A key set of information about this change was archived in the snow that fell at that time in Antarctica. At unique locations, that snow is still preserved today in the deepest ice layers– but does it still contain its original message? AiCE will answer this key question specifically using chemical impurity signals which make up a large part of the ice core record about past atmospheric conditions. For this purpose, we take a new approach to study the oldest and highly thinned layers at unprecedented detail. While conventional meltwater analysis delivers 1D cm-resolution signals, we go into 2D by imaging the chemical impurity distribution at micro-metric scale in the solid ice core. This way, we can retrieve crucial information that is inevitably lost by melting: The same ice matrix preserving the climatic record can act on it and ultimately destroy it through various processes, causing impurities to relocate away from their original layer. Hence, the goal is to identify the original layering, by detecting post-depositional change through analyzing highly-dimensional chemical images. However, human observers have clear limitations in detecting all the important details in such complex visual datasets. This is why AiCE will add deep learning to deep ice: Artificial intelligence (Ai) image analysis will be established through a comprehensive understanding of the chemical image features and their connection to post-depositional processes. With this, we can address the fundamental climate questions through deciphering deep ice – in Antarctica and elsewhere. Ultimately, AiCE could revolutionize how we interpret the oldest paleoclimate signals in ice cores and other archives.

Mots-clés : polar ice cores,ice core impurities,ice core record preservation,laser ablation,computer vision,beyond epica
Projet ERC : TerraForm (Horizon Europe, appel 2024, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Study of the Human Past
Entité participante : Université de Malte — Pays : Malte — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1628045.0 €

Human-modified landscapes modulate the effects of climate change on human societies. For example, concrete surfaces increase flood risks and dense cities exacerbate the effects of warming. However, urbanisation is not the only major form of landscape modification. Agricultural terraces represent one of the largest-scale human modifications of the planet, and have many positive effects for cultivation, soil retention, carbon and water storage, and biodiversity. Despite their profound importance for soil modification and a sustainable future, the causes, chronology, and consequences of terracing are all poorly understood, as is the impact of their growing abandonment. This limits our ability to understand how climate change will impact human societies, and the extent to which terraces can help build resilience and sustainability. Pilot research suggests that terracing may have had a uniquely early development on the Maltese Islands, which ultimately became one of the most heavily terraced landscapes on Earth. TerraForm will therefore use Malta as a model system for understanding the evolution and consequences of terracing, from inception to abandonment, alongside societal feedbacks over time. Through its small size, remote location, and limited soil development, Malta offers controlled conditions for such a study. As Malta sits at the front line of climate change, understanding terracing here also has urgent implications for soil erosion, food security, and flooding - both regionally and in areas facing analogous futures. Finally, many of Malta’s terraces have recently been abandoned and are rapidly collapsing, resulting in the loss of an important archive for climate mitigation. Using Malta as a model system is therefore timely, and as the most intensive single-locus study of terracing ever conducted, TerraForm will generate multidisciplinary data and transferable methodologies to ultimately inform sustainable land use policies and improve planetary health.

Mots-clés : anthropocene archaeology archaeology of climate change mediterranean history mediterranean prehistory archaeology of terracing geoarchaeology island archaeology
Projet ERC : INSPIRE (Horizon Europe, appel 2021, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and Communication Engineering
Entité participante : Centre national de la recherche scientifique — Pays : France — Rôle : PI
Acronyme de l'entité : CNRS
Type d'entité : Recherche
Financement ERC de l'entité : 1918858.0 €

Neural networks revolutionize computation and already outperform humans according to many benchmarks. They have the potential to transform society and have already influenced Google to place artificial intelligence at the core (“AI first”) of business strategy. Governments and policy bodies worldwide focus on neural network-based computing as a vital future technology.  Despite the evident promise, however, current architectures severely lose computational efficiency when applied to large neural networks and scale badly. The performance of current neural network hardware is orders of magnitude below what is theoretically possible, and the future development of artificial intelligence is therefore in jeopardy.  I will solve this problem by developing neural network processors using advanced photonic components, specifically enabling a breakthrough with three-dimensional integrated photonic waveguides to implement a biologically inspired, fully parallel and scalable architecture. This is not an incremental improvement, but rather a completely new and never-explored approach, which will revolutionize neural network hardware. Based on this technology, I will create a fully programmable optical tensor processing unit by interfacing 3D waveguide Mach-Zehnder interferometers with a commercial SLM. The unit will calculate the connection of a neural network with speeds and energy efficiency exceeding the state of the art by two orders of magnitude, while programmability makes it widely applicable. A second system will go far beyond by implementing fully-fletched and programmable optical neural networks based on 3D waveguides and semiconductor lasers.  The proof of principle building blocks of the project are in place and I am recognized as a leading researcher in the field. The realisation of this project will unlock doors to scalability, cascadability, and parallelism, and stimulate new research as well as applications in artificial intelligence.