Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : EPIC (Horizon 2020, appel 2017, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : University of Edinburgh — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1499351.0 €

In the future, high-efficiency (low CO2) vehicles will be powered in part by reinvented internal combustion (IC) engines that are “downsized” and operate with new combustion modes. These engine concepts are subject to problems such as increased transient heat transfer and flame quenching in small passages. Near-wall transient heat transfer is not well-understood in engine environments; the gas is not constant in pressure, temperature, or velocity such that physical processes quickly digress from established theory. EPIC is uniquely placed to address these problems. A novel constant-volume chamber, offering realistic engine passages but with optical access, and which emulates the pressure/temperature time curve of a real engine, will be developed. This chamber will make it possible to measure the highly transient and highly variable processes at the gas/wall interface (including a highly dynamic flame front) for single- and two-wall passages. Measurements will be made using a suite of advanced laser diagnostics; a novel aspect of the proposed work as they have not been used in combination to study such a problem before. Hybrid fs/ps rotational coherent Raman (i.e. CARS) in a line format will provide transient gas temperature and species profiles normal to the wall surface in high-risk/high-gain packages. PIV/PTV measurements will further elucidate flow dynamics at the surface. Planar OH-LIF will help interpret CARS measurements and provide necessary details of flame transport and quenching. As the flame approaches the surface, phosphor thermometry will measure wall temperature and heat flux to elucidate the highly dynamic inter-coupling between flame and wall. EPIC will provide substantial breakthroughs in knowledge by measuring unsteady boundary layer development and understanding its influence on flame quenching for single- and two-wall surfaces. As such, EPIC will provide the fundamental knowledge that supports cleaner combustion technology for the future.

Mots-clés : laser diagnostic measurements,heat transfer,flame quenching
Projet ERC : PAINSTRAT (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : Italian Institute of Technology — Pays : Italie — Rôle : PI
Type d'entité : Recherche
Financement ERC de l'entité : 1181995.86 €

In the last 15 years the advances in understanding the molecular mechanisms of pain have been spectacular. However, despite the >200 genes identified and the dramatic insights on transduction and transmission of nociceptive signals, very little has changed in the diagnosis and treatment of pain, at least in terms of analgesic drugs. Therefore, pain remains a major burden for the society (chronic pain alone costs the EU >€250 bns/yr). The reason is that pain is not a pattern of gene expression, but a state of neuronal activity. Therefore, the translation of mechanistic insights into effective treatments will depend critically on a better understanding of the electrophysiological changes that occur. For this reason, the European Federation of Neurological Society has called for a particular effort to identify reliable laboratory measures of pain, and having an objective biomarker for pain quantification and patient stratification would constitute a major breakthrough in medicine. To date, this need has not been satisfied. The project has two main objectives: (1) Developing neurophysiological biomarkers of pain perception: the main aim being quantification of spontaneous pain for patient management and clinical trials. (2) Developing neurophysiological biomarkers of the susceptibility to develop chronic pain: the main aim being patient stratification, development of prevention strategies, and personalised medicine for pain. We will apply novel analysis techniques to the neurophysiological information obtained using a range of techniques from a large number of healthy volunteers and patients with different types of chronic pain. Importantly, we will longitudinally study patients at high risk of developing chronic pain. We are fully confident that these goals - early diagnosis, identification of high risk patients and facilitating the development of new efficacious treatments through useful clinical trials - will positively impact the well-being of chronic pain patients.

Mots-clés : personalized medicine,patient stratification,chronic pain,neurophysiology
Projet ERC : BroadSem (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : University of Edinburgh — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1289942.5 €

In the last one or two decades, language technology has achieved a number of important successes, for example, producing functional machine translation systems and beating humans in quiz games. The key bottleneck which prevents further progress in these and many other natural language processing (NLP) applications (e.g., text summarization, information retrieval, opinion mining, dialog and tutoring systems) is the lack of accurate methods for producing meaning representations of texts. Accurately predicting such meaning representations on an open domain with an automatic parser is a challenging and unsolved problem, primarily because of language variability and ambiguity. The reason for the unsatisfactory performance is reliance on supervised learning (learning from annotated resources), with the amounts of annotation required for accurate open-domain parsing exceeding what is practically feasible. Moreover, representations defined in these resources typically do not provide abstractions suitable for reasoning. In this project, we will induce semantic representations from large amounts of unannotated data (i.e. text which has not been labeled by humans) while guided by information contained in human-annotated data and other forms of linguistic knowledge. This will allow us to scale our approach to many domains and across languages. We will specialize meaning representations for reasoning by modeling relations (e.g., facts) appearing across sentences in texts (document-level modeling), across different texts, and across texts and knowledge bases. Learning to predict this linked data is closely related to learning to reason, including learning the notions of semantic equivalence and entailment. We will jointly induce semantic parsers (e.g., log-linear feature-rich models) and reasoning models (latent factor models) relying on this data, thus, ensuring that the semantic representations are informative for applications requiring reasoning.

Mots-clés : natural language processing
Projet ERC : SeleCHEM (Horizon 2020, appel 2017, 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 : PI
Acronyme de l'entité : ETH Zurich
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2000000.0 €

In the last two centuries, synthetic organic chemistry has undergone an unprecedented revolution. The ability to understand and modify the molecular structure of matter has changed our life in many areas, such as medicine, agriculture or commodity materials. These major successes gave the impression that synthetic chemistry is a mature field. However, this impression is completely misleading, as current synthetic methods still lack the selectivity needed for the modification of complex molecules. Both selecting between different reactive groups and functionalizing inert bonds in their presence represent formidable challenges. In this project, we propose to develop highly selective “molecular tethers” for the functionalization of both natural/synthetic organic compounds and biomolecules. The envisioned tethers are bifunctional small organic molecules having three fundamental properties: 1) A “biting end” with unique reactivity to be selectively installed in situ onto naturally occurring thiols, alcohols and amines. We will use tethers based on acetals and hypervalent iodine reagents. 2) A “functional end”, whose reactivity can be revealed “at will” to functionalize bonds that cannot be accessed with the current state of the art of synthetic chemistry, especially inert C-H and C=C bonds. 3) Being traceless, meaning that they can be removed easily once the desired functionalization has been achieved. The main impact of this project will be in fundamental synthetic organic chemistry, as it will contribute to overcoming major selectivity hurdles in the functionalization of complex molecules. It will therefore result in faster progress in all the fields depending on synthetic molecules, such as medicine, agriculture or materials. A more efficient functionalization of biomolecules will allow us to soften the boundaries between synthetic chemistry and biology, leading to major progress in our understanding of living systems and our ability to modify them.

Mots-clés : late stage functionalization,new synthetic methods,c h bond and olefin functionalization,biomolecule functionalization,radicals,photoredox,selectivity,covalent linkers,hypervalent iodine
Projet ERC : REGFUT (Horizon 2020, appel 2020, Advanced grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : University of Guadalajara — Pays : Mexique — Rôle : Partner
Acronyme de l'entité : UDG
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 159690.0 €

In the last two decades, an information revolution in the global south has profoundly shaped the urbanisation of metropolitan regions. Global and national initiatives to adopt smart technologies in local governments, with the claim that opportunities presented by digitalisation will resolve the challenges of urbanisation – are now literally automating regional futures. This project will conduct the first comprehensive South-South investigation of the dynamics of digitalisation-as-urbanisation – the transition to automated planning processes in metropolitan regions, and its impacts on regional urbanisation. The project will conduct research in peri-urban municipalities of three rapidly growing metropolitan regions of the global south where municipal digitalisation is directed towards strategic regional planning. These municipalities face major challenges with transforming paper-based colonial and postcolonial bureaucracies into automated planning processes within highly unequal contexts, and therefore represent the wider experience of digitalisation-as-urbanisation in the global south. Through detailed ethnography, interviews and information audit trails in digitalising municipalities, the project will investigate a) the rescaling of governance to the local digitalising state; b) the territorialisation of information infrastructures; and c) territorial politics of digitalisation. It will examine how digitalisation produces new territories for regional urbanisation and how state and non-state actors are assisting, contesting and disrupting these regional futures. It will bring to fruition the applicant’s agenda setting work on postcolonial urban futures, smart cities, digital citizenships and recent work on the governance of small cities in the global south. The project will build new theories and detailed empirical evidence of southern urbanisation as both a product and a producer of the ‘information revolution’ in the global south.

Mots-clés : digitalising state,regional future,urbanisation,information revolution,municipal governance
Projet ERC : UNIQUE (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed matter physics
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 : 1550627.0 €

In the majority of optoelectronic devices emission and absorption of light are considered as perturbative phenomena. The objective of my project is to explore the ultra-strong light-matter coupling regime in a new type of optoelectronic semiconductor-based devices operating in the THz and MIR frequency range (lambda=3-300µm). These devices will allow the first time experimental observation of intrinsically quantum features of the ultra-strong coupling regime, such as quantum vacuum radiation (dynamical Casimir effect) and squeezing of polaritons states. In these devices, generically acting as detectors, the light-matter coupled states (polaritons) will be efficiently converted into electrical signals. The matter excitation is based on the electronic transitions in semiconductor quantum wells, where the light-matter interaction is strongly enhanced owe to collective effects. To achieve the ultra-strong coupling regime, the collective electronic excitation is coupled with metamaterial nano-resonator acting as high frequency inductor-capacitive circuit. In such resonator, very high electric field intensity is achieved into effective volume of sizes comparable with the electron De Broglie wavelength. The photoconductivity of such detectors will be dominated by polariton–assisted fermionic transport. The metamaterial detectors will be supplied with sensitive read-out based on the single-electron transistor concept, which will allow the observation of quantum vacuum radiation as well as the non-classical photo-counting statistics of polaritons. In these device architectures I will also implement the dynamical Coulomb blockade, where the single electron charging energy e²/2C becomes comparable with the metamaterial resonator energy ћw. This effect will be exploited as a disruptive approach to sense the quantum-optical properties of light-matter coupled states by all-electronic means.

Mots-clés : ultra strong light matter coupling,metamaterials,quatum wells,electronic transport,quantum optics
Projet ERC : STAMFORD (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : Aarhus University — Pays : Danemark — Rôle : Partner
Acronyme de l'entité : AU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150498.65 €

In the past twenty years the availability of vast dimensional data, typically referred to as big data, has given rise to exciting challenges in various fields of mathematics and computer sciences. The increasing need for getting a better understanding of such data in internet traffic, biology, genetics, and economics, has lead to a revolution in statistical and machine learning, optimisation and numerical analysis. Due to high dimensionality of modern statistical models, parameter estimation is a difficult task and statisticians typically investigate estimation methods under sparsity constraints. While an abundance of estimation algorithms is now available for high dimensional discrete models, a rigorous mathematical investigation of estimation problems for high dimensional continuous-time processes is completely undeveloped. The aim of STAMFORD is to provide a concise statistical theory for estimation of high dimensional diffusions. Such high dimensional processes naturally appear in modelling particle interactions in physics, neural networks in biology or large portfolios in economics, just to name a few. The methodological part of the project will require development of novel advanced techniques in mathematical statistics and probability theory. In particular, new results will be needed in parametric and non-parametric statistics, and high dimensional probability, that are reaching far beyond the state-of-the-art. Hence, a successful outcome of STAMFORD will not only have a tremendous impact on statistical inference for continuous-time models in natural and applied sciences, but also strongly influence the field of high dimensional statistics and probability.
Projet ERC : PiRaT (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : KTH Royal Institute of Technology — Pays : Suède — Rôle : PI
Acronyme de l'entité : KTH
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2000000.0 €

In the past two decades great progress has been made on the understanding of the remarkable patterns that random tilings of planar domains exhibit. Yet, many models are still out of reach with state-of-the-art techniques and several conjectures remain unsolved. The general purpose of this project is develop new techniques for solving such conjectures and explore new territories. In particular we will look at random tilings models where the randomness is comes from doubly periodic weights on the underlying bipartite graph and their connection to matrix valued special functions. The project includes the following 6 objectives: 1. Develop methods for asymptotic studies of the correlation function for random tilings of large domains, including measures from doubly periodic weights. 2. Derive new asymptotic formulas for matrix-valued orthogonal polynomials by developing a steepest descent method for their Riemann-Hilbert problem. 3. Formulate and investigate natural extensions of Schur processes that include doubly periodic weights that have a special integrable structure, such as the two-periodic Aztec diamond. 4. Study the universality of global fluctuations of the height functions. 5. Prove new Central Limit Theorems fluctuations of linear statistics with for determinantal especially those coming from random tilings. 6. A deeper investigation of the random geometry of the height fluctuations, such as level lines and thick points.

Mots-clés : random tilings,determinantal point processes,log correlated fields,orthogonal polynomials
Projet ERC : SoftHand Pro-H (Horizon 2020, appel 2016, Proof of concept grants)
Entité participante : Qbrobotics srl — Pays : Italie — Rôle : Partner
Type d'entité : Organismes privés (hors enseignement)
Financement ERC de l'entité : 73750.0 €

In the project “SoftHands” we have achieved not only a more thorough understanding of the organization and control of hands, but also a principled approach to taming the complexity of hand design. The original concept of “soft synergies” has underpinned the realization of radically new artificial hands: “SoftHands” have been demonstrated to be more adaptive and capable than most artificial hands, yet are simpler to control and more robust. One application of these ideas and technologies which stands out for potential impact and social relevance, although not originally foreseen in the ERC AG plan, is the realization of upper limb pros-theses. The objective to realize a prosthetic hand that is anthropomorphic, aesthetically pleasing, and enables an amputee to perform most activities of daily living as well as advanced prostheses, while being robust, intuitive, and economic as basic body-powered split-hook prostheses requires a much longer and larger re-search and development effort than an ERC POC can support. In this proposal we study the feasibility of applying the SoftHand technology to address one particular, but very important, objective, i.e. work-oriented prostheses. The specific requirements of these applications are high grip power, grasp versatility, resilience, resistance to water, dust, and temperature, durability, power autonomy and low cost – while factors such as aesthetics or silent operation are less dominant. Of particular relevance is the control interface with the patient. Virtually all work-oriented prostheses are operated via a body-powered cable, which is very intuitive to use and does not need batteries, motors, and sensors. On the other hand, advanced multi-fingered prostheses have sophisticated myoelectric control affording versatility and dexterity. In this project, we will engineer and experiment a novel hybrid control for a SoftHand prosthe-sis, whereby a traditional cable harness commands the advanced mechatronic system of the SoftHand.

Mots-clés : robotics,prosthetics,rehabilitation,assitive technology
Projet ERC : LICCI (Horizon 2020, appel 2017, Consolidator grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : Autonomous University of Barcelona — Pays : Espagne — Rôle : PI
Acronyme de l'entité : UAB
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1999999.0 €

In the quest to better understand local climate change impacts on physical, biological, and socioeconomic systems and how such impacts are locally perceived, scientists are challenged by the scarcity of grounded data, which has resulted in a call for exploring new data sources. People with a long history of interaction with the environment have developed complex knowledge systems that allow them to detect local impacts of climatic variability, but these insights are absent in climate change research and policy fora. I will bring insights from local knowledge to climate research by 1) providing data on local climate change impacts on physical (e.g., shrinking glaciers) and biological systems (e.g., phenological changes) and on perceptions of climate change impacts on socioeconomic systems (e.g., crop failure due to rainfall patterns change) and 2) testing hypotheses on the global spatial, socioeconomic and demographic distribution of local climate change impacts indicators. Research will last five years. The first 18 months, Preparation, I will train a team who will develop and implement a data collection protocol and design a web-based platform where citizens can enter information on local climate change impacts indicators. During the following two years, Data collection, we will train 40 external PhD students to collect project’s data in data-deficient regions and disseminate the platform. During the last 18 months, Analysis, the core team will use spatial matching and multivariate analysis to test hypotheses related to the spatial, socioeconomic, and demographic distribution of local climate change impacts indicators. External PhD students will analyse local data. Dissemination will be transversal to the project. This project will fill theoretical and spatial gaps on climate change impacts research. It will also improve local capacity to respond to climate change impacts and help bridge epistemological differences between local and scientific knowledge systems.