Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : METACELL (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems Engineering
Entité participante : CIC biomaGUNE — Pays : Espagne — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 1995894.0 €

One of the major challenges of sustainable chemistry is expanding the palette of bio-based chemicals that can replace, or at least ameliorate, the exploitation of fuel-based chemicals. Cell-free metabolic engineering using soluble enzymes is an emerging and versatile approach that seeks to increase the selectivity and productivity of chemical biomanufacturing processes. However, soluble and isolated enzymes present major issues in terms of efficiency, stability and re-usability that hamper industrial applications. To solve these problems, enzymes can be rationally immobilized on smart materials resulting in robust, efficient and self-sufficient heterogeneous biocatalysts, but immobilization is still restricted to simple enzyme cascades. METACELL mission is developing self-sufficient artificial metabolic cells (AMCs) by immobilizing complex metabolic networks on hierarchical porous materials. To this aim, the solid surfaces must play an active role in the chemical process rather than just being a mere immobilization support. This integrative proposal will exploit protein engineering, surface chemistry, bio-organic chemistry and protein immobilization tools for the successful development of 1) a cell-free artificial metabolism, 2) innovative engineering tools to modify both enzyme and material surfaces and 3) continuous synthesis of industrially relevant fine chemicals catalyzed by AMCs packed into flow reactors. The resulting technology of METACELL will serve as a prototyping platform to test artificial biosynthetic pathways with application in combinatorial chemistry (e.g drugs discovery). METACELL may also offer long-term solutions for the on-demand production of drugs at the point-of-care. In addition to the technological outputs, METACELL will also provide essential information to understand how spatial organization of multi-enzyme systems affect the performance of in vitro biosynthetic pathways confined into artificial chassis (solid materials).

Mots-clés : enzymes,protein immobilization,protein engineering,flow biocatalysis,synthetic chemistry
Projet ERC : SeeSuper (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed matter physics
Entité participante : Aarhus University — Pays : Danemark — Rôle : PI
Acronyme de l'entité : AU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1089820.95 €

One of the major outstanding challenges in condensed matter physics is the origin of high temperature superconductivity. Low temperature BCS superconductivity is mediated by the electron-phonon interaction, but this interaction is believed to be too weak to explain high temperature superconductivity. Instead electron interactions are considered responsible, but experimental proof has been difficult to obtain. Despite over thirty years of research, the mechanism responsible for generating the superconducting state still remains unknown. SeeSuper aims to break this deadlock by applying new experimental techniques to study the superconducting state. Our strategy is to probe high temperature superconductors through their nanoscale and femtosecond fluctuations. We will focus on three key parameters in superconductors: phonons, spins and nanoscale phase separation, with the aim of revealing the coupling mechanism. Our approach combines transient optical spectroscopy and time-resolved diffuse X-ray scattering to measure the lattice response to large amplitude coherent vibrations, time-resolved non-linear optical spectroscopy to directly probe spin dynamics, and resonant soft X-ray holography to image dynamics on the nanoscale. We will use these cutting edge techniques to prove our hypothesis, that lattice anharmonicity is the key missing ingredient to explain the origins of high temperature superconductivity. If demonstrated, the impact of such a result will lead to a step-change in our understanding of how superconductivity at high temperature occurs, help guide the search for materials with higher transition temperatures, and influence how we view and understand a much broader class of materials. Furthermore, the experimental techniques that we will develop can be applied to understand a range of materials and will, therefore, have an impact also on the broader field of condensed matter physics.

Mots-clés : superconductivity,anharmonicity,coherent phonons,x ray holography,optical spectroscopy,phase separation,spin dynamics
Projet ERC : EPIC (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : University of Liège — Pays : Belgique — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2078125.0 €

One of the most ambitious goals of modern astrophysics is to characterise the physical and chemical properties of rocky planets orbiting in the habitable zone of nearby Sun-like stars. Although the observation of planetary transits could in a few limited cases be used to reach such a goal, it is widely recognised that only direct imaging techniques will enable such a feat on a statistically significant sample of planetary systems. Direct imaging of Earth-like exoplanets is however a formidable challenge due to the huge contrast and minute angular separation between such planets and their host star. The proposed EPIC project aims to enable the direct detection and characterisation of terrestrial planets located in the habitable zone of nearby stars using ground-based high-contrast imaging in the thermal infrared domain. To reach that ambitious goal, the project will focus on two main research directions: (i) the development and implementation of high-contrast imaging techniques and technologies addressing the smallest possible angular separations from bright, nearby stars, and (ii) the adaptation of state-of-the-art machine learning techniques to the problem of image processing in high-contrast imaging. While the ultimate goal of this research can likely only be reached with the advent of giant telescopes such as the Extremely Large Telescope (ELT) around 2025, the EPIC project will lay the stepping stones towards that goal and produce several high-impact results along the way, e.g. by re-assessing the occurrence rate of giant planets in direct imaging surveys at the most relevant angular separations (i.e., close to the snow line), by conducting the deepest high-contrast imaging search for rocky planets in the alpha Centauri system, by preparing the scientific exploitation of the ELT, and by providing the first open-source high-contrast image processing toolbox relying on supervised machine learning techniques.

Mots-clés : exoplanets,high contrast imaging,image processing,deep learning,stellar coronagraphy,wavefront control,infrared instrumentation
Projet ERC : HeBe (Horizon 2020, appel 2020, Starting grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : Barcelonabeta — Pays : Espagne — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 1498915.0 €

One of the most exciting findings in neuroscience has been the discovery of peripheral blood factors with rejuvenating or ageing effects on the mouse brain. These factors were discovered by parabiosis experiments in which the blood circulation of young and old animals is fused. The findings imply that the properties of young blood can rejuvenate the older brain. Ageing is the most important risk factor for Alzheimer’s Disease (AD), but there have been only limited attempts to develop therapies targeting the ageing process. Also, it is not known whether humans have similar blood factors that could be therapeutic targets in AD. The overall aim of the HeBe project is to discover blood factors with a rejuvenating or ageing effect on the human brain that can thus become therapeutic targets for AD and other age-related brain diseases. For ethical reasons, parabiosis experiments are not possible in humans. HeBe will circumvent this problem using a highly original approach in which I will define extreme biological age phenotypes in women and men based on the difference between biological and chronological age. I will use advanced proteomics and metabolomics to identify blood factors that differ between these extreme phenotypes, and then develop a targeted assay toolkit to measure these factors in large longitudinal cohorts of cognitively unimpaired and preclinical AD individuals. The main hypothesis of HeBe is that higher levels of rejuvenating blood factors decrease the rate of neurodegeneration, while higher levels of ageing blood factors increase the rate of neurodegeneration. If this hypothesis is confirmed, I will provide a new therapeutic target for AD. By the end of HeBe, I will also design a proof-of-concept clinical trial to assess whether life-style or pharmacological interventions modify blood levels of rejuvenating or ageing factors. Thus, the HeBe project is the first and a key step in the translational continuum towards interventions in clinical practice.

Mots-clés : alzheimer s disease,clinical neurology,neurodegeneration,biomarkers,dementia,translational research
Projet ERC : NegCap (Horizon 2020, appel 2017, Proof of concept grants)
Entité participante : Tel Aviv University — Pays : Israël — Rôle : PI
Acronyme de l'entité : TAU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

One of the most exciting proposed solutions to the approaching limit of down-scaling in microelectronics (so-called “end of Moore’s law”) consists of the use of negative capacitors, [NegCap]s. When a NegCap is placed between the gate and the channel of a FET (field-effect transistor), the applied voltage on the gate is strongly amplified on the channel. Therefore, it is possible to reduce substantially the applied gate voltage, yet keeping the voltage on the gate sufficiently high for on/off switching of the FET. To continue the scaling down of basic electronic components such as FET, reduction of the applied voltage is essential in order to avoid overheating due to the too high power dissipation in the ultra high-density circuits. On the other hand, due to fundamental laws of physics, a minimum voltage of 60mV/decade is necessary for operation of the FET with currently available technologies. A way to solve these conflicting needs is to reduce the gate voltage and in parallel use a NegCap that will amplify the voltage on the channel. But how to get a negative capacitor? In 2008 Salahuddin and Datta proposed to make NegCap using a ferroelectric capacitor. However, so far, experimental attempts failed to show the stable negative capacitance necessary for the voltage amplification on the FET gate. The key problem is that the ferroelectric splits into domains, which cancels the stabilized negative capacitance effect. In our ERC-AdG project (“MOBILE-W”), we conceived a concept, supported by theory and modelling, that allows the fabrication of negative capacitors in which splitting to domains is prohibited. Here we aim to demonstrate this experimentally providing proof of concept, and find suitable framework and partners to translate our concept into commercial products. We believe that this will solve one of the major road-blocks for further scaling down of microelectronic circuits.

Mots-clés : negative capacitance,ferroelectrics,transistor,nc fet
Projet ERC : ShimBSD (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : University of Warwick — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1739143.0 €

One of the most famous open problems in mathematics is the Birch–Swinnerton-Dyer (BSD) conjecture, which predicts that the size of the set of rational points on an elliptic curve is determined by the order of vanishing at s = 1 of its Hasse–Weil L-function. Building a crucial breakthrough due to Kolyvagin in the 1990's—the discovery of the first example of an "Euler system"—the BSD conjecture has now been proved for a wide class of elliptic curves over the rationals: those where the order of vanishing of the L-function (the "analytic rank") is 0 or 1, which conjecturally accounts for 100% of elliptic curves. However, the case of elliptic curves over the rationals is only the tip of an iceberg. Versions of the BSD conjecture are also expected to hold for elliptic curves over number fields, and more generally for abelian varieties of any dimension (with elliptic curves being the case of dimension 1). Even more generally, the Bloch–Kato conjecture predicts that for any L-function arising from geometry, its order of vanishing at any integer point encodes geometric information. However, these conjectures are far beyond the reach of Kolyvagin's Euler system. The aim of my proposal is to prove new cases of the BSD conjecture and the Bloch–Kato conjecture, using new Euler systems arising from the geometry of unitary and symplectic Shimura varieties. In particular, I will prove the rank 0 case of the BSD conjecture for abelian surfaces over the rationals, elliptic curves over imaginary quadratic fields, and abelian three-folds with complex multiplication, assuming appropriate modularity results hold for these objects (which are known in many cases).

Mots-clés : msc2010  11g40  l functions of varieties over global fields,birch swinnerton dyer conjecture
Projet ERC : PODCAST (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : École normale supérieure de Lyon — Pays : France — Rôle : PI
Acronyme de l'entité : ENS de Lyon
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1723662.0 €

One of the most fascinating and challenging question of Modern Astrophysics is: How do planets form? Indeed, micronic dust grains must grow over 30 orders of magnitude in mass to build planet cores. Global numerical simulations of dust grains that couple the dynamics of the particles to their growth/fragmentation and the radiation in the disc are compulsory to understand this process. Yet, this coupling has never been realised, given tremendous difficulties that originate from fundamental physical properties of dusty flows. The evolution of the dust distribution in protoplanetary discs remains therefore very poorly understood. Our novel groundbreaking code is the first to handle non-ideal MHD, radiation and dust with dynamical growth and fragmentation. We can therefore overcome all past difficulties to model gasgrains mixtures in discs consistently. PODCAST is designed to study the different stages of gas and dust evolution in the various regions of the disc, with the main objective of combining these steps in a holistic model for planet formation. We will confront the results directly with observations, unleashing the full potential of the grand instruments ALMA, SPHERE, JWST and SKA.
Projet ERC : ChemLife (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Trinity College Dublin — Pays : Irlande — Rôle : PI
Acronyme de l'entité : TCD
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499887.0 €

One of the most interesting properties of living organisms is the way in which they can sense and respond to changes by moving. Movement has been essential to the survival of all life; even units as small as cells can react to different chemicals through movement. This is a phenomenon known as chemotaxis. Bacteria use chemotaxis to find sources of food, while white blood cells use chemotaxis to follow a chemical trail left by a virus, then find it and destroy it. Throughout areas of science, from robotics to drug delivery, if we could mimic a fraction of this fascinating complexity, the possibilities would be endless. Imagine micro-structured vehicles, which could ‘navigate’ through complex fluidic environments, and could effectively ‘recognise’, ‘sense’, ‘diagnose’ and ‘treat’ a variety of conditions. This is exactly what this proposed project, ChemLife, will explore. I will make smart droplets which travel through complicated mazes by chemotaxis, communicate with each other, and move to find their partners or locate and neutralise a ‘droplet intruder’. Other biological systems have much more complicated means of movement, such as swimming, crawling or gliding along surfaces. In an attempt to replicate this, I will fabricate ‘swimmers’ and ‘crawlers’, from soft materials which will move independently and travel through liquids or at the bottom of fluidic channels. Not only will these micro-vehicles be able to travel inside fluids, but they will also be able to detect molecules, signal to other vehicles, and repair problems which they encounter. They underpin a key ambition of ChemLife: the realisation of a Biomimetic Toolbox, a library of adaptable vehicles, which can be demonstrated in a wide range of scenarios. The assembly of these micro-vehicles in to ‘smart’ societies which can perform complicated tasks would be a really exciting achievement, with the potential to become a disruptive foundational breakthrough for movement and transport at the micro-scale.

Mots-clés : stimuli responsive materials,microfluidics,chemotaxis,3d printing,hydrogels
Projet ERC : ONOFF (Horizon 2020, appel 2015, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Complexity
Entité participante : University of Bergen — Pays : Norvège — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2413371.0 €

One of the most perplexing phenomena of the human mind is the conviction of perceiving a "voice" in the absence of an external auditory source. This is called an auditory hallucination (AH), and is the most characteristic symptom of the most severe mental disorder, schizophrenia. Understanding the phenomenology, cognitive, and neurobiological underpinnings of AHs will not only provide new insights for explaining schizophrenia, but will also provide new insights into the "complexities of the mind". In my previous research, I have uncovered the neurocognitive markers of the initiation of an AH, focusing on what causes the onset of a hallucinatory episode. The current proposal further narrows the focus, asking a single question; why do AHs spontaneously come-and-go over time, or stated otherwise, why are they not permanently present once initiated? If we believe that the onset of an episode has neurocognitive markers, which all evidence supports, then we must also acknowledge that the offset must have corresponding markers. This question has to my knowledge never been addressed, although it is vital for the understanding of the underlying mechanisms, and for new treatment strategies. I will track the fluctuations of AH episodes in real-time with iPhone app technology, going beyond interview questionnaires. I will track how cognition modulates the onset and offset with an experimental dichotic listening paradigm, going beyond standard tests. I will track what happens in the brain the few seconds before the onset and, in particular the offset of an episode, using component-based fMRI analysis, going beyond "blobology". I will track with MR spectroscopy the interaction of excitatory and inhibitory transmitters that are hypothesized to mediate episode onsets and offsets, respectively, going beyond systems imaging. Thus, I will track AH episodes from the clinical to the receptor level, working vertically through a “levels of explanation” model, from higher to lower levels

Mots-clés : auditory hallucinations,perception,attention,smartphone app,cognition,neuroimaging,neurochemistry
Projet ERC : TheoryDL (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Hebrew University of Jerusalem — Pays : Israël — Rôle : PI
Acronyme de l'entité : HUJI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1342500.0 €

One of the most significant recent developments in applied machine learning has been the resurgence of ``deep learning'', usually in the form of artificial neural networks. The empirical success of deep learning is stunning, and deep learning based systems have already led to breakthroughs in computer vision and speech recognition. In contrast, from the theoretical point of view, by and large, we do not understand why deep learning is at all possible, since most state of the art theoretical results show that deep learning is computationally hard. Bridging this gap is a great challenge since it involves proficiency in several theoretic fields (algorithms, complexity, and statistics) and at the same time requires a good understanding of real world practical problems and the ability to conduct applied research. We believe that a good theory must lead to better practical algorithms. It should also broaden the applicability of learning in general, and deep learning in particular, to new domains. Such a practically relevant theory may also lead to a fundamental paradigm shift in the way we currently analyze the complexity of algorithms. Previous works by the PI and his colleagues and students have provided novel ways to analyze the computational complexity of learning algorithms and understand the tradeoffs between data and computational time. In this proposal, in order to bridge the gap between theory and practice, I suggest a departure from worst-case analyses and the development of a more optimistic, data dependent, theory with ``grey'' components. Success will lead to a breakthrough in our understanding of learning at large with significant potential for impact on the field of machine learning and its applications.

Mots-clés : deep learning,algorithms,statistics,computational complexity