Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : ORIENT (Horizon 2020, appel 2015, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Complexity
Entité participante : Radboud University Nijmegen — Pays : Pays-Bas — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2209688.0 €

Rapid object identification is crucial for survival of all organisms, but poses daunting challenges if many stimuli compete for attention, and multiple sensory and motor systems are involved in the processing, programming and generating of an eye-head gaze-orienting response to a selected goal. How do normal and sensory-impaired brains decide which signals to integrate (“goal”), or suppress (“distracter”)? Audiovisual (AV) integration only helps for spatially and temporally aligned stimuli. However, sensory inputs differ markedly in their reliability, reference frames, and processing delays, yielding considerable spatial-temporal uncertainty to the brain. Vision and audition utilize coordinates that misalign whenever eyes and head move. Meanwhile, their sensory acuities vary across space and time in essentially different ways. As a result, assessing AV alignment poses major computational problems, which so far have only been studied for the simplest stimulus-response conditions. My groundbreaking approaches will tackle these problems on different levels, by applying dynamic eye-head coordination paradigms in complex environments, while systematically manipulating visual-vestibular-auditory context and uncertainty. I parametrically vary AV goal/distracter statistics, stimulus motion, and active vs. passive-evoked body movements. We perform advanced psychophysics to healthy subjects, and to patients with well-defined sensory disorders. We probe sensorimotor strategies of normal and impaired systems, by quantifying their acquisition of priors about the (changing) environment, and use of feedback about active or passive-induced self-motion of eyes and head. I challenge current eye-head control models by incorporating top-down adaptive processes and eye-head motor feedback in realistic cortical-midbrain networks. Our modeling will be critically tested on an autonomously learning humanoid robot, equipped with binocular foveal vision and human-like audition.

Mots-clés : multisensory integration,spatial perception,dynamic feedback control
Projet ERC : E-SAC (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : TU Wien — Pays : Autriche — Rôle : PI
Acronyme de l'entité : TUW
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1993718.0 €

Rare and expensive metals tend to be the best catalysts, and minimising or replacing them is a major research target as we strive to develop an economy based on more environmentally-friendly, energy-efficient technologies. “Single-atom” catalysis (SAC) represents the ultimate in efficiency, and the chemical bonds formed between the metal atom and the support mean these systems strongly resemble the organometallic complexes utilized in homogeneous catalysis. If all active sites were identical, single-atom catalysts (SACs) could achieve similar levels of selectivity, and even be used to “heterogenize” difficult reactions that must be currently performed in solution. There is a problem however: homogeneous catalysts are designed based on well-understood structure-function relationships. In SAC, the structure of the active site is unknown, thus rational design is impossible. My group in Vienna has pioneered the use of the model supports to understand fundamental mechanisms in SAC. Our work with Fe3O4(001) proves that we can precisely determine and even selectively modify the active site, and unravel the role of structure in catalytic activity. Real progress, however, requires realistic active sites, realistic supports, and realistic environments. In this project, I describe how we will determine the sites that robustly anchor metal atoms on five of the most important supports in ultrahigh vacuum (UHV), and test their performance in newly-developed high-pressure and electrochemical cells. The origins of selectivity for PROX, hydrogenation, hydroformylation, methane conversion, and the oxygen reduction reaction (ORR) will be elucidated, and the best atom/support combinations for each reaction identified. Robust XANES and IRAS spectra will allow us to bridge the complexity gap and recreate the optimal active sites on real SACs and lead the way into a new era in which heterogeneous catalysts are designed for purpose, based on a fundamental understanding of how they work.

Mots-clés : single atom catalysis
Projet ERC : QNets (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : Forschungszentrum Jülich — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : FZJ
Type d'entité : Recherche
Financement ERC de l'entité : 1486439.0 €

Reaching a fundamental understanding of quantum many-body systems and fully harnessing their computational power for information processing is one of today’s greatest scientific challenges. To date, unprecedented research efforts are underway to build quantum devices, which would outperform the most powerful classical computers. At the same time, neural networks are currently revolutionising the handling of large amounts of data, with enormous success in pattern and speech recognition, machine learning, the analysis of ‘big data’ and ‘deep learning’. Driven by the hope of combining massive parallel information processing in neural networks with quantum advantages like computational speedup, there have been various efforts to develop quantum neural networks – without satisfactory answers to date. The overarching goal of this theoretical research programme is to tackle this enormous challenge from a fresh perspective: we will establish and explore a conceptual framework for quantum neural networks and identify quantum optical physical building blocks, based on concepts in the domain of open many-body quantum systems. This ambitious aim will be achieved by interlinking a multitude of scientific areas ranging from atomic physics, quantum optics, quantum engineering and condensed matter physics to quantum information and computer science. This research will not only generate a genuine step change in our fundamental understanding of the ways nature allows for quantum information processing. It will also lay the foundation for quantum neuromorphic engineering of a new generation of quantum neural hardware in state-of-the-art and newly emerging experimental systems of ultra-cold atoms and trapped ions. With my interdisciplinary background in quantum information and quantum engineering, quantum optics and atomic physics, I am in a unique position to successfully realise this research. I will also strongly benefit from the vital scientific environment at Swansea University.

Mots-clés : quantum engineering,rydberg atoms,trapped ions,quantum neural networks
Projet ERC : QNets (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : Swansea University — Pays : Royaume-Uni — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 0.0 €

Reaching a fundamental understanding of quantum many-body systems and fully harnessing their computational power for information processing is one of today’s greatest scientific challenges. To date, unprecedented research efforts are underway to build quantum devices, which would outperform the most powerful classical computers. At the same time, neural networks are currently revolutionising the handling of large amounts of data, with enormous success in pattern and speech recognition, machine learning, the analysis of ‘big data’ and ‘deep learning’. Driven by the hope of combining massive parallel information processing in neural networks with quantum advantages like computational speedup, there have been various efforts to develop quantum neural networks – without satisfactory answers to date. The overarching goal of this theoretical research programme is to tackle this enormous challenge from a fresh perspective: we will establish and explore a conceptual framework for quantum neural networks and identify quantum optical physical building blocks, based on concepts in the domain of open many-body quantum systems. This ambitious aim will be achieved by interlinking a multitude of scientific areas ranging from atomic physics, quantum optics, quantum engineering and condensed matter physics to quantum information and computer science. This research will not only generate a genuine step change in our fundamental understanding of the ways nature allows for quantum information processing. It will also lay the foundation for quantum neuromorphic engineering of a new generation of quantum neural hardware in state-of-the-art and newly emerging experimental systems of ultra-cold atoms and trapped ions. With my interdisciplinary background in quantum information and quantum engineering, quantum optics and atomic physics, I am in a unique position to successfully realise this research. I will also strongly benefit from the vital scientific environment at Swansea University.

Mots-clés : quantum engineering,rydberg atoms,trapped ions,quantum neural networks
Projet ERC : ALGOCom (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Aalto University — Pays : Finlande — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1411258.0 €

Real-world optimization problems pose major challenges to algorithmic research. For instance, (i) many important problems are believed to be intractable (i.e. NP-hard) and (ii) with the growth of data size, modern applications often require a decision making under {\em incomplete and dynamically changing input data}. After several decades of research, central problems in these domains have remained poorly understood (e.g. Is there an asymptotically most efficient binary search trees?) Existing algorithmic techniques either reach their limitation or are inherently tailored to special cases. This project attempts to untangle this gap in the state of the art and seeks new interplay across multiple areas of algorithms, such as approximation algorithms, online algorithms, fixed-parameter tractable (FPT) algorithms, exponential time algorithms, and data structures. We propose new directions from the {\em structural perspectives} that connect the aforementioned algorithmic problems to basic questions in combinatorics. Our approaches fall into one of the three broad schemes: (i) new structural theory, (ii) intermediate problems, and (iii) transfer of techniques. These directions partially build on the PI's successes in resolving more than ten classical problems in this context. Resolving the proposed problems will likely revolutionize our understanding about algorithms and data structures and potentially unify techniques in multiple algorithmic regimes. Any progress is, in fact, already a significant contribution to the algorithms community. We suggest concrete intermediate goals that are of independent interest and have lower risks, so they are suitable for Ph.D students.

Mots-clés : approximation algorithms,hardness of approximation,data structures,online algorithms,combinatorial optimization
Projet ERC : COMMIOS (Horizon 2020, appel 2018, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The study of the human past
Entité participante : University of Glasgow — Pays : Royaume-Uni — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 194628.0 €

Recent breakthroughs in ancient DNA and isotope analysis are transforming our understanding of diversity, mobility and social dynamics in the human past. COMMIOS integrates these cutting-edge methods on a scale not previously attempted, within a ground-breaking interdisciplinary framework, to provide a radically new vision of Iron Age communities in Britain (800 BC – AD 100) within their wider European context. At the broad scale, we will conduct the first concerted programme of genome-wide ancient DNA analysis on Iron Age populations anywhere in the world (c. 1000 individuals in the UK, 250 in Europe), mapping genetic clusters to shed light on ancient populations themselves and on their relationships to modern genetic patterning. Together with isotope analysis, and underpinned by both osteoarchaeological and cultural archaeological approaches, this will also enable us to directly address critical issues of population movement and inter-regional connectivity in Iron Age Europe. We will utilise the power of these new scientific methods to examine the structure and social dynamics of Iron Age societies in Britain, including household and kin-group composition, the identification of familial relationships, gender-specific mobility, and the development of social inequalities. Previously the preserve of cultural anthropologists studying recent societies, we will draw these questions into the archaeological domain, opening up new areas of enquiry for prehistoric societies. The scope and scale of the project represents a new departure for European archaeology, made possible by the coming-of-age of new analytical methods. Many of these have been pioneered by the project team, which comprises world-leaders in the fields of ancient DNA, isotope analysis, osteoarchaeology, chronological modelling and cultural archaeology. Although focussed on Iron Age Britain, the project will establish a new benchmark for future analyses of other regions and periods in Europe and beyond.

Mots-clés : iron age britain,iron age europe,ancient dna,isotope analysis,osteoarchaeology,social dynamics,funerary archaeology
Projet ERC : FORMICA (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Life Science
Panel : Biotechnology and Biosystems Engineering
Entité participante : Ghent University — Pays : Belgique — Rôle : PI
Acronyme de l'entité : UGent
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1498469.0 €

Recent global warming is acting across ecosystems and threatening biodiversity. Yet, due to slow responses, many biological communities are lagging behind warming of the macroclimate (the climate of a large geographic region). The buffering of microclimates near the ground measured in localized areas, arising from terrain features such as vegetation and topography, can explain why many species are lagging behind macroclimate warming. However, almost all studies ignore the effects of microclimatic buffering and key uncertainties still exist about this mechanism. Microclimates are particularly evident in forests, where understorey habitats are buffered by overstorey trees. In temperate forests, the understorey contains the vast majority of plant diversity and plays an essential role in driving ecosystem processes. The overall goal of FORMICA (FORest MICroclimate Assessment) is to quantify and understand the role of microclimatic buffering in modulating forest understorey plant responses to macroclimate warming. We will perform the best assessment to date of the effects of microclimates on plants by applying microtemperature loggers, experimental heating, fluorescent tubes and a large-scale transplant experiment in temperate forests across Europe. For the first time, plant data from the individual to ecosystem level will be related to microclimate along wide temperature gradients and forest management regimes. The empirical results will then be integrated in cutting-edge demographic distribution models to forecast plant diversity in temperate forests as macroclimate warms. FORMICA will provide the first integrative study on microclimatic buffering of macroclimate warming in forests. Interdisciplinary concepts and methods will be applied, including from climatology, forestry and ecology. FORMICA will reshape our current understanding of the impacts of climate change on forests and help land managers and policy makers to develop urgently needed adaptation strategies.

Mots-clés : climate change,forest ecology,population and community ecology,forestry,functional biodiversity,global change biology,macro ecology,management,statistical modeling
Projet ERC : METABOLIGHT (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Board of the Swiss Federal Institutes of Technology — Pays : Suisse — Rôle : Partner
Acronyme de l'entité : ETH Zurich
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 0.0 €

Recent investigations revealed that cancer tissue possesses several important metabolic features, such as differential utilization of many essential metabolites. This phenomenon is now referred to as "metabolic reprogramming" and is known to be required for malignant transformation, tumor development, invasion and metastasis. Its complex and dynamic nature has been recognized as one of the major challenges for effective cancer treatment. Therefore, a better understanding of metabolic dependencies in specific tumor types can provide a path for improved cancer treatments. However, no efficient methodologies currently exist that allow noninvasive imaging and quantification of the uptake of essential metabolites in animal models of disease. Current strategies rely on either nuclear imaging techniques such as PET/SPECT/MRI or endpoint ex vivo quantification of metabolites (ex. MS). All of these methods have significant limitations, resulting in a lack of understanding of tumor metabolism and, consequently, poor predictability and efficacy of cancer therapies. To address the unmet need for nutrient uptake imaging tools, we propose to develop a novel platform based on a combination of versatile “click” chemistry reactions with noninvasive, ultrasensitive bioluminescent imaging techniques. The applications of the novel platform will focus on tumor uptake measurements of glucose, fatty acids, amino acids, and nucleosides previously reported to play important roles in cancer metabolism. We will then use this platform to investigate the metabolic reprogramming of different liver cancers, which may lead to the generation of novel, effective treatments; therefore, this novel platform has high clinical applicability. Due to its versatile nature, application of the platform can be expended to studies of many other important human pathologies in which changes in metabolism play a key role, such as diabetes, neurodegenerative diseases, nonalcoholic steatohepatitis, and many others

Mots-clés : optical imaging,cancer metabolic reprogramming,metabolite uptake
Projet ERC : METABOLIGHT (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Swisslumix sarl — Pays : Suisse — Rôle : PI
Type d'entité : Organismes privés (hors enseignement)
Financement ERC de l'entité : 1997375.0 €

Recent investigations revealed that cancer tissue possesses several important metabolic features, such as differential utilization of many essential metabolites. This phenomenon is now referred to as "metabolic reprogramming" and is known to be required for malignant transformation, tumor development, invasion and metastasis. Its complex and dynamic nature has been recognized as one of the major challenges for effective cancer treatment. Therefore, a better understanding of metabolic dependencies in specific tumor types can provide a path for improved cancer treatments. However, no efficient methodologies currently exist that allow noninvasive imaging and quantification of the uptake of essential metabolites in animal models of disease. Current strategies rely on either nuclear imaging techniques such as PET/SPECT/MRI or endpoint ex vivo quantification of metabolites (ex. MS). All of these methods have significant limitations, resulting in a lack of understanding of tumor metabolism and, consequently, poor predictability and efficacy of cancer therapies. To address the unmet need for nutrient uptake imaging tools, we propose to develop a novel platform based on a combination of versatile “click” chemistry reactions with noninvasive, ultrasensitive bioluminescent imaging techniques. The applications of the novel platform will focus on tumor uptake measurements of glucose, fatty acids, amino acids, and nucleosides previously reported to play important roles in cancer metabolism. We will then use this platform to investigate the metabolic reprogramming of different liver cancers, which may lead to the generation of novel, effective treatments; therefore, this novel platform has high clinical applicability. Due to its versatile nature, application of the platform can be expended to studies of many other important human pathologies in which changes in metabolism play a key role, such as diabetes, neurodegenerative diseases, nonalcoholic steatohepatitis, and many others

Mots-clés : optical imaging,cancer metabolic reprogramming,metabolite uptake
Projet ERC : PhaseAge (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Life Science
Panel : Cellular, Developmental and Regenerative Biology
Entité participante : Max Planck Society — Pays : Allemagne — Rôle : Partner
Acronyme de l'entité : MPG
Type d'entité : Recherche
Financement ERC de l'entité : 646501.94 €

Recent studies suggest that defective ribonucleoprotein (RNP) granules and altered RNA processing cause neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). However, defective RNP granules only develop late in life, suggesting that young cells have mechanisms in place to prevent their formation. We recently demonstrated that physiological RNP granules form through phase separation in the cytoplasm and adopt a liquid-like state, but with time transition into an aberrant disease-associated state with solid material properties. Focussing on this conceptual advance, we will 1) investigate the molecular mechanisms of RNP granule formation; 2) pinpoint the molecular events that lead to aberrant RNPs, focusing on disease-associated mutations, changes in environmental conditions, post-translational modifications and molecules with fluidizing or solidifying effects; and 3) define the mechanisms of RNP quality control, which prevent aberrant phase transitions or reverse RNP aggregates to their normal state, thus rescuing a cell from an otherwise fatal condition. Key to the project are recently developed methodologies to reconstitute RNPs from purified proteins and RNAs, and biophysical techniques to analyse the material properties of RNP granules. Combined with our ability to perform time-resolved studies of RNPs in living cells and our thematic focus on the link between RNP dynamics and functionality, this project represents a systematic and realistic approach to tackling the essential question of how RNP granules form and why they cause disease. We expect that our findings will have impact far beyond ALS and FTD, because aberrant phase transitions may be at the heart of many protein-misfolding diseases. We envision that our findings will lead to new therapeutic interventions that may significantly improve the prospects of patients afflicted with these diseases.

Mots-clés : rnp granules,rna metabolism,phase transition,protein aggregation,proteostasis,aging,neurodegenerative disease