Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : FAT-BC (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Life Science
Panel : Prevention, Diagnosis and Treatment of Human Diseases
Entité participante : KU Leuven — Pays : Belgique — Rôle : PI
Acronyme de l'entité : KUL
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1998620.0 €

One out of 8 women develops breast cancer (BC) in her lifetime and 1 out of 2 is overweight or obese in industrialized countries. While heavier women have an increased risk of developing BC and heavier BC patients present with worse disease characteristics, BC is so far still treated regardless of patient adiposity because of the limited knowledge accumulated so far. I hypothesize that increased adiposity provides a different ‘soil’ for the tumor cells (the 'seeds') to grow, potentially leading to biological peculiarities of the tumor cells and the cells from the tumor microenvironment. To investigate this hypothesis, I will firstly study the associations between patient adiposity and the genomic and transcriptomic features of the bulk tumor in >2.700 BC patients (FATomics). Secondly, I will complement these findings at the single cell level by creating a comprehensive atlas of BC according to various patient and mammary adiposity measures for the two most common BC histological subtypes (FATlas). Special attention will be given to the mammary fat cells, the adipocytes, and how these help tumor cells to grow and affect treatment efficacy. State-of-the-art technologies such as single-nuclei RNA sequencing, spatial protein visualization and digital pathology will be applied. Thirdly, the FATlas data will be shared with the scientific community through a web-based dataportal (FATshare). Finally, using samples from a prospective in-house clinical trial, I will investigate whether adiposity-associated features are associated with anti-proliferative and/or immune response to immunotherapy and endocrine therapy (FATrial). FAT-BC integrates complementary and multidisciplinary approaches with state-of-the-art technologies and will build upon my expertise in BC biology. The results should lead to the identification of potential strategies to tailor BC treatment according to adiposity, a still unmet clinical need in the context of personalized medicine.

Mots-clés : breast cancer,obesity,genomics,transcriptomics,immune infiltrates,single cell,single nuclei rna sequencing,clinical trial
Projet ERC : CELPRED (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Life Science
Panel : Neuroscience and Disorders of the Nervous System
Entité participante : Friedrich Miescher Institute — Pays : Suisse — Rôle : PI
Acronyme de l'entité : FMI
Type d'entité : Recherche
Financement ERC de l'entité : 2000000.0 €

One promising theoretical framework to explain the function of cortex is predictive processing. It postulates that cortex functions by maintaining an internal model, or internal representation, of the world through a comparison of predictions based on this internal model with incoming sensory information. Implementing predictive processing in a cortical circuit would require a set of distinct functional cell types. These would include neurons that compute a difference between top-down predictions and bottom-up input, referred to as prediction error neurons, and a separate population of neurons that integrate the output of prediction error neurons to maintain an internal representation of the world. This research proposal will test the framework of predictive processing and identify different putative circuit elements and cell types that are thought to form the circuit in mouse visual cortex. We will use a combination of physiological recordings, optogenetic manipulations of neural activity, and gene expression measurements to determine the cell types that have functional responses consistent with different prediction errors, as well as those coding for the internal representation. Identifying the circuit elements underlying predictive processing in cortex may reveal a strategy to bias processing either towards top-down or bottom-up drive when the balance between the two is perturbed, as may be the case in neuropsychiatric disorders.

Mots-clés : predictive processing,cortex,neural computation
Projet ERC : FLATBANDS (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : Max Planck Society — Pays : Allemagne — Rôle : Partner
Acronyme de l'entité : MPG
Type d'entité : Recherche
Financement ERC de l'entité : 0.0 €

One spectacular phenomenon in quantum many-body systems is the emergence of non-local quasiparticles with fractional quantum numbers and anyonic statistics. Of fundamental interest, fractionalization also holds promise for fault-tolerant quantum computation motivating the search for such exotic phases of matter. Signatures of this phenomenon remain sparse and mostly restricted to fractional quantum Hall states, despite being predicted to also occur in other systems such as frustrated quantum magnets. An essential feature shared by both systems is the massive ground state degeneracy, or flat band, out of which fractionalization emerges. The underlying non-local topological order of such phases is an outstanding experimental challenge to detect with only local observables. Building on my experience to study "hidden" order in one dimensional systems, I will address the physics of strong correlations in two and three dimensional flatbands using ultracold atoms and the unique probes of atomic physics. I propose in FLATBANDS to build a novel strontium quantum gas microscope to study both fractional quantum Hall states and highly frustrated magnets. I will first rotate mesoscopic dilute Bose gases to mimic the behaviour of electrons in magnetic fields. Using observables down to individual particles, I will study density-density correlations in the lowest Landau level, providing signatures of emerging Laughlin-like states. On the same platform, I will measure spin correlations and detect fractionalization in highly frustrated magnets. Using atoms trapped in programmable tweezer arrays and excited in Rydberg states, I will engineer quantum spin-ice and directly observe the emergence of magnetic monopoles. By following two complementary routes to address strong correlations and topological order, FLATBANDS will open fascinating perspectives with impact in quantum information, quantum computation, and condensed matter physics.

Mots-clés : quantum gases,quantum simulation,quantum gas microscope,strongly correlated systems
Projet ERC : REBOUND (Horizon 2020, appel 2018, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
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 : 2490087.0 €

Online media is an important part of modern information society, offering a podium for public discourse and hosting the opinions of hundreds of millions of individuals. Online media are often credited for providing a technological means to break information barriers and promote diversity and democracy. In practice, however, the opposite effect is often observed: users tend to favor content that agrees with their existing world-view, get less exposure to conflicting viewpoints, and eventually create information silos and increased polarization. Arguably, without any kind of mediation, current social-media platforms gravitate towards a state in which net-citizens are constantly reinforcing their existing opinions. In this project we will develop theoretical foundations and a concrete set of algorithmic techniques to address deficiencies in today's online media. We will develop methods to discover structure and patterns of segregation, conflict, and closeness in social-media systems. We will address the issues of reducing bias and polarization, breaking information silos, and creating awareness of users to explore alternative viewpoints. We will also study the effect of different design features to the willingness of the users to explore viewpoints that conflict their opinion. The project is structured along three interwined research thrusts: knowledge discovery, exploration, and content recommendation. To accomplish its aims the project will formulate novel problem representations that provide a deeper understanding of the undesirable phenomena observed in online media and allow for effective remedial actions. Strong emphasis will be given on designing algorithms that are scalable to large data, are able to deal with uncertainty, and offer theoretical guarantees. The end result will be a set of new methods and tools that will contribute to increasing exposure to diverse ideas and improving online deliberation.

Mots-clés : data mining,graph mining,social network analysis,social media analysis,information networks,combinatorial and probabilistic algorithms
Projet ERC : WINDMIL RT-DT (Horizon 2020, appel 2018, Proof of concept grants)
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 : 148890.0 €

Operation & Maintenance (O&M) costs may account for 30 % of the total cost of energy for offshore wind power. Alarmingly, only after a few years of installation, offshore wind turbines (WT) may need emergency repairs. They also feature an extremely short lifespan hindering investments to green energy, effectively designed to reduce CO2 emissions. We have designed real-time monitoring and diagnostics platform in the context of operation and maintenance scheduling of WT components. Using this architecture, we can quantify the risk of future failure of a given component and trace back the root-cause of the failure. This is business-critical information for Energy Companies and Wind Farm Operators. The platform consists of an autonomous software-hardware solution, implementing an Object Oriented Real-Time Decision Tree learning algorithm for smart monitoring and diagnostics of structural and mechanical WT components. The innovative concept lies in running WT telemetry data through a machine learning based decision tree classification algorithm in real-time for detecting faults, errors, damage patterns, anomalies and abnormal operation. We believe our innovation creates evident value and will raise great interest as decision-support tool for WT manufacturers, Wind Farm Operators, Service Companies and Insurers. In this project, we will carry out pre-commercialisation actions to position ourselves in the market, provide unique selling proposition for future customers as well as raise interest among potential R&D collaborators and pilot customers. We will also establish technology proof of concept for the platform. For the first time, we are applying our design in difficult-to-access energy infrastructure installations and deploying it on a real-world prototype wind turbine. The project will be carried out with technical and commercialisation support from key players within the wind energy industry.

Mots-clés : commercialisation,decision support tool,sw hw platform,wind turbine,machine learning,artificial intelligence,decision tree classification,monitoring,root cause analysis,failure prediction,cloud computing,business critical information,energy company,wind farm
Projet ERC : DC-THz (Horizon 2020, appel 2020, Proof of concept grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
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 : 150000.0 €

Optical absorption spectroscopy is a key enabling technology for contact-free and non-destructive identification of materials. Since these measurements allow for identifying defects or contaminants in products, they are used in many industries. Examples include testing of polymers, coatings and paints, pharmaceuticals, foods, and semiconductor devices for electronics. Optical spectroscopy also has emerging applications in environmental monitoring, security, and medical diagnostics. Light in the terahertz region of the electromagnetic spectrum (frequencies from 0.1 – 10 THz) is particularly attractive for all these applications because of its remarkable properties: it can be transmitted through many materials, allowing for inspection of products inside their packaging; its interaction with matter leaves unique spectral fingerprints, enabling identification of the molecules present; and it offers good resolution, enabling imaging applications. However, a significant challenge for existing industrial terahertz spectroscopy systems is the high cost and complexity of the driving laser. In this ERC-PoC project, we will resolve these problems by leveraging our recently-demonstrated breakthrough dual-comb laser technology. These patented dual-comb lasers produce asynchronous high-power femtosecond pulses from a single laser cavity. This solution combines higher performance and lower complexity compared to the alternative technologies. The goal of the ERC-PoC is to deploy these advantages in the context of terahertz spectroscopy to enable faster measurements and lower costs, and take important steps to commercialization. We will develop a prototype industrial terahertz spectrometer, measure test samples with research and industrial collaborators, and find initial product-market fit for commercializing the spectrometer.

Mots-clés : terahertz time domain spectroscopy,dual optical frequency combs,non destructive testing,ultrashort pulse lasers
Projet ERC : SiNBioSys (Horizon 2020, appel 2017, Proof of concept grants)
Entité participante : University of Bologna — Pays : Italie — Rôle : PI
Acronyme de l'entité : UNIBO
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 149937.5 €

Optical imaging is a powerful tool for light-guided surgery and for the study of biological functions via dynamic visualization of processes in tissues and cells. Most of optical imaging techniques utilise fluorescent probes that can suffer from low contrast, due to scattering of the excitation light, and low tissue penetration. Silicon nanocrystals (SiNCs), developed within the ERC PhotoSi project, are a very promising alternative for this purpose: silicon is essentially non-toxic, easily available, and it can be covalently linked with dyes. The result is a highly-robust and biocompatible hybrid material, which exhibits colour tunability across the visible and near-infrared region. On top of that, the extraordinary brightness of the material coupled to a long-lived luminescence (lifetime of the hundreds of microseconds) enables time-gated detection. Therefore, SiNBioSys technology greatly improves the contrast of the obtained images with a low-cost equipment and with a material based on abundant and biocompatible elements (Si, C, O, H). An international patent application on the key underlying technology was filed in 2015 and subsequently nationalized in the primary markets (Europe and USA). Based on these premises, this proposal is intended to gather sufficient technical and commercial evidence to make the SiNBioSys technology attractive for further investment in the development of a new commercial product by: (i) optimization and validation of SiNCs against current gold standards; (ii) an in-depth market analysis; (iii) a realistic business development strategy with demonstrations to leading companies in the field. The proposed technology will thus bring an economic impact, as well as social benefits: it will demonstrate its ability to replace currently used expensive setups and will enable innovative imaging tools for early diagnosis of diseases, particularly in the field of cancer and neuroscience.

Mots-clés : quantum dots,luminescence,diagnostics,time gated detection,bioimaging
Projet ERC : See-Beyond (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
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 : 1999875.0 €

Optical imaging systems play an instrumental role in our modern life, from smartphones and automotive cameras to microscopes that are critical for clinical diagnostics. However, the penetration depth of even the most advanced systems is still unbearably limited by the inherent random scattering of light in complex samples. Examples span many applicative fields, including scattering in tissues and fog, limiting microscopes and laser-based systems. For decades, the notion of correcting scattering seemed unfeasible since it requires control of billions of optical modes. This conception changed a decade ago with the paradigm-shifting revolution of wavefront-shaping, demonstrating that scattering can be physically corrected using spatial light modulators with a number of pixels orders-of-magnitude smaller than the number of scattered modes. Wavefront-shaping led to astonishing breakthroughs, including my own works, from focusing through visually-opaque barriers to imaging around corners. However, beyond laboratory demonstrations, there is a fundamental gap in applying these revolutionary notions in most practical imaging applications, as wavefront-shaping is based on a physical, inherently 2D, limited-speed correction to a volumetric dynamic scattering problem, and it relies on known ‘guide-stars’ at the target. I propose a radically different route to remove these fundamental barriers and unleash the full applicative potential of wavefront-shaping, by shifting the burden from the physical hardware to a digital, naturally-parallelizable computational process. My approach is based on computationally emulating the optimal 3D wavefront-correction, found using only a few unique rapid holographic measurements. My solution is enabled by our recent discovery of guide-star free wavefront-shaping, where the target themselves serve as guide-stars, and the great increase in computational power. Its impact spans across important domains, from endoscopy to non-line-of-sight imaging.

Mots-clés : optical wavefront shaping,light in complex media,scattering,optical microscopy,micro endoscopy
Projet ERC : DYNA-MIC (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
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 : 1500000.0 €

Optical microscopy, perhaps the most important tool in biomedical investigation and clinical diagnostics, is currently held back by the assumption that it is not possible to noninvasively image microscopic structures more than a fraction of a millimeter deep inside tissue. The governing paradigm is that high-resolution information carried by light is lost due to random scattering in complex samples such as tissue. While non-optical imaging techniques, employing non-ionizing radiation such as ultrasound, allow deeper investigations, they possess drastically inferior resolution and do not permit microscopic studies of cellular structures, crucial for accurate diagnosis of cancer and other diseases. I propose a new kind of microscope, one that can peer deep inside visually opaque samples, combining the sub-micron resolution of light with the penetration depth of ultrasound. My novel approach is based on our discovery that information on microscopic structures is contained in random scattered-light patterns. It breaks current limits by exploiting the randomness of scattered light rather than struggling to fight it. We will transform this concept into a breakthrough imaging platform by combining ultrasonic probing and modulation of light with advanced digital signal processing algorithms, extracting the hidden microscopic structure by two complementary approaches: 1) By exploiting the stochastic dynamics of scattered light using methods developed to surpass the diffraction limit in optical nanoscopy and for compressive sampling, harnessing nonlinear effects. 2) Through the analysis of intrinsic correlations in scattered light that persist deep inside scattering tissue. This proposal is formed by bringing together novel insights on the physics of light in complex media, advanced microscopy techniques, and ultrasound-mediated imaging. It is made possible by the new ability to digitally process vast amounts of scattering data, and has the potential to impact many fields.

Mots-clés : optical microscopy,light in complex media,scattering,optical wavefront shaping,super resolved imaging,compressive sensing
Projet ERC : RYD-QNLO (Horizon 2020, appel 2017, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Fundamental constituents of matter
Entité participante : University of Southern Denmark — Pays : Danemark — Rôle : Partner
Acronyme de l'entité : SDU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 792332.0 €

Optical photons, for all practical purposes, do not interact. This fundamental property of light forms the basis of modern optics and enables a multitude of technical applications in our every-day life, such as all-optical communication and microscopy. On the other hand, an engineered interaction between individual photons would allow the creation and control of light photon by photon, providing fundamental insights into the quantum nature of light and allowing us to harness non-classical states of light as resource for future technology. Mapping the strong interaction between Rydberg atoms onto individual photons has emerged as a highly promising approach towards this ambitious goal. In this project, we will advance and significantly broaden the research field of Rydberg quantum optics to develop new tools for realizing strongly correlated quantum many-body states of photons. Building on our successful work over recent years, we will greatly expand our control over Rydberg slow-light polaritons to implement mesoscopic systems of strongly interacting photons in an ultracold ytterbium gas. In parallel, we will explore a new approach to strong light-matter coupling, utilizing Rydberg superatoms made out of thousands of individual atoms, strongly coupled to a propagating light mode. This free-space QED system enables strong coupling between single photons and single artificial atoms in the optical domain without any confining structures for the light. Finally, we will experimentally realize a novel quantum hybrid system exploiting the strong electric coupling between single Rydberg atoms and piezo-electric micro-mechanical oscillators. Building on this unique coupling scheme, we will explore Rydberg-mediated cooling of a mechanical system and dissipative preparation of non-classical phonon states. The three complementary parts ultimately unite into a powerful Rydberg quantum optics toolbox which will provide unprecedented control over single photons and single phonons.