Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Towards the ultimate dark matter detector (ULTIMATE)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Fundamental constituents of matter
Panel description: Particle, nuclear, plasma, atomic, molecular, gas, and optical physics
Countries involved: Germany
Call: ERC Consolidator Grant (ERC-2016-COG, 2016)
Start date: 2017-05-01
End date: 2022-10-31
Duration (months): 66.0
Total project cost (EUR): 1982938.0
EU funding (EUR): 1982938.0
Participants: 1
Project status: CLOSED

Dark matter is a major component of the Universe, outnumbering ordinary baryonic matter by a factor 5. As it has not yet been observed, its detection and subsequent characterization is one of the most important goals in particle physics. ULTIMATE will be the first project world-wide that focuses entirely on cutting-edge research towards the ultimate detector. Using a low-background time projection chamber (TPC) filled with ~40t of liquid xenon (LXe), this instrument will search for Galactic dark matter in the form of Weakly Interacting Massive Particles (WIMPs). It will probe the entire experimentally accessible parameter space above masses of a few GeV/c², which is eventually limited by irreducible neutrino backgrounds. If dark matter will be detected by the next-generation experiments, the ultimate detector will deliver a high statistics WIMP sample to study its properties. To eventually propose and build this detector, which will be also sensitive to many non-WIMP science channels, various fundamental experimental challenges need to be solved now. These include a significant reduction of radioactive backgrounds, which would seriously limit the instrument's sensitivity, and structural aspects related to the TPC size of ~2.5m. ULTIMATE will tackle both, following several orthogonal strategies: Two novel TPC concepts will be developed and operated in LXe for the first time, to reduce background from 222Rn (hermetic TPC) and to optimize background rejection (single-phase TPC). Background neutrons and 222Rn emanation from the important material PTFE will be minimized by the identification of radio-pure PTFE, a systematic study of surface treatments, and by building a full-scale TPC mockup. Such prototype has not been constructed before and will enable detailed design, construction and assembly studies of a TPC with minimal material budget. The combination of all strategies explored in ULTIMATE will represent an optimal concept for the ultimate WIMP detector's TPC.

Keywords: astroparticle physics,dark matter,rare event search,liquid noble gas detector
Agile Analytics on Big Data Cubes (EarthServer-2)

Framework: Horizon 2020 — Excellent Science
Programme: Research Infrastructures
Theme: Research infrastructures
Destination: e-Infrastructures
Action type: Research and Innovation actions
Countries involved: Australia,Germany,Italy,Greece,United Kingdom,United Kingdom
Call: Managing, preserving and computing with big research data (H2020-EINFRA-2014-2, 2014)
Start date: 2015-05-01
End date: 2018-04-30
Duration (months): 36.0
Total project cost (EUR): 2839743.75
EU funding (EUR): 2839743.0
Participants: 7
Project status: CLOSED

EarthServer-2 makes Agile Analytics on Big Earth Data Cubes of sensor, image, simulation, and statistics data a commodity for non-experts and experts alike through • navigation, extraction, aggregation, and recombining of any-size space/time data cubes; • easy to install & maintain value-adding services extending the existing portfolio of data and compute centers; • based on open standards, in particular: the OGC Big Data standards and the forthcoming ISO SQL/MDA (“Multi-Dimensional Arrays”) standard. In the Joint Research Activity, the project will advance the existing, world-leading rasdaman Array Database technology wrt. query functionality, inter-federation data processing with automatic data and query distribution, tape archive integration, and 3D/4D visualization based on NASA’s virtual globe technology. In the Services Activity, large data centers (ECMWF, PML, MEEO/ESA, GeoScience Australia, JacobsUni) will set up water, air, weather, and planetary services on 3D & 4D data cubes up to Petabyte-size with user-tailored clients for both visual and textual ad-hoc mix&match. In the Networking Activity, the project will advance open Big Data standards in OGC, RDA, and ISO (in particular: write ISO SQL/MDA). Further, all adequate channels will be used for strong dissemination & exploitation, specifically: writing a monograph explaining OGC Big Geo Data standards; scientific publications & active conference organization; Earth science data user workshops for each domain addressed; actively contributing technology & experience to GEO / GEOSS and further bodies; establish standardized Big Geo Data benchmark and run it against EarthServer-2 and further relevant systems. Altogether, EarthServer-2 will maintain and extend the lead in Big Earth Data services established in the highly successful EarthServer-1 project. Being already supported by ESA, rasdaman will form an enabling building block for COPERNICUS / Sentinel.

Keywords: array databases,copernicus,big data,data cube,science data,e science infrastructures
Open Deep Learning Toolkit for Robotics (OpenDR)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Action type: Research and Innovation actions
Countries involved: Germany,Spain,Finland,Switzerland,Greece,Netherlands,Denmark
Call: Robotics Core Technology (H2020-ICT-2019-2, 2019)
Start date: 2020-01-01
End date: 2023-12-31
Duration (months): 48.0
Total project cost (EUR): 6661685.0
EU funding (EUR): 6661685.0
Participants: 8
Project status: SIGNED

The aim of OpenDR is to develop a modular, open and non-proprietary tool kit for core robotic functionalities by harnessing deep learning to provide advanced perception and cognition capabilities, meeting in this way the general requirements of robotics applications in the applications areas of Healthcare, Agri-Food and Agile Production. The term toolkit in OpenDR refers to a set of deep learning software functions, packages and utilities used to help roboticists to develop and test a robotic application that incorporates deep learning. OpenDR will provide the means to link the robotics applications to software libraries (deep learning frameworks, e.g., tensorflow) and to link it with the operating environment (ROS). OpenDR focuses on the AI and Cognition core technology in order to provide tools that make robotic systems cognitive, giving them the ability to a) interact with people and environments by developing deep learning methods for human centric and environment active perception and cognition, b) learn and categorise by developing deep learning tools for training and inference in common robotics settings, and c) make decisions and derive knowledge by developing deep learning tools for cognitive robot action and decision making. As a result, the developed OpenDR toolkit will also enable cooperative human-robot interaction as well as the development of cognitive mechatronics where sensing and actuation are closely coupled with cognitive systems thus contributing to another two core technologies beyond AI and Cognition. OpenDR will develop, train, deploy and evaluate deep learning models that improve the technical capabilities of the core technologies beyond the current state of the art. It will enable a greater range of robotics applications that can be demonstrated at TRL 3 and above, thus lowering the technical barriers within the prioritised application areas.

Keywords: ai and cognition,deep learning for robotics,robotic autonomy,active robot perception,open source toolkit
Artificial Intelligence and IoT for tree health monitoring (TreeVoice)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Italy
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2018)
Start date: 2018-10-01
End date: 2019-03-31
Duration (months): 6.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

Treevoice is an ambitious and unique project exploiting IoT, Artificial Intelligence, M2M technologies and Structural Health Monitoring techniques to enable predictive maintenance of green areas and telecommunication infrastructures in urban and extra-urban scenarios, ensuring real time analysis of trees health and detecting harmful conditions leading to trees or wooden pole falls, which are a severe cause of damage to both citizens and the environment. The innovative sensor manufactured by Treevoice S.r.l. is able to sense and identify deviations in the normal vibration patterns, temperature and other environmental parameters in the wood, while the sophisticated machine learning algorithms in the cloud allow to predict and prevent falling events, while also helping reducing direct and indirect costs related to accidents caused by falls and maintenance of green areas and infrastructures in Smart Cities, boosting green growth. Treevoice S.r.l. has already developed and tested several prototypes of the innovative tree sensor, together with the software infrastructure necessary for data collection and analysis, and aims to perform a sound testing and validation study on a real urban environment. The first on the field extensive experimentation will take place in the municipality of Rome, where the installation of 100 sensors is going to start. The company will develop a detailed technical and business feasibility study during Phase 1, focusing on the industrialization of the system, performing the validation and testing campaign by installing 100 tree sensors, deeper training the machine learning algorithms in the cloud enabling new services, while also consolidating and refining the Business Model, assessing the achievable markets, and profiling and involving customers, investors and key stakeholders.

Keywords: predictive maintenance of green areas,environmental monitoring,iot technology
The first predictive in vitro assay for the identification of respiratory sensitizers. (GARDair)

Framework: Horizon 2020 — Societal Challenges
Programme: Health, demographic change and wellbeing
Theme: Treating and managing disease
Action type: SME instrument
Countries involved: Sweden
Call: Supporting innovative SMEs in the healthcare biotechnology sector (H2020-SMEINST-2-2016-2017, 2016)
Start date: 2017-03-01
End date: 2019-08-31
Duration (months): 30.0
Total project cost (EUR): 2421800.0
EU funding (EUR): 2421800.0
Participants: 1
Project status: CLOSED

The GARDair project will optimize and validate the first predictive assay for respiratory sensitization and bring it to market. This innovative in vitro test builds on the successes of its forerunner – the GARDskin assay, which has been shown to hold the highest predictive power (89%) of skin sensitizers amongst all commercially available methods. The GARD technology offers an unparalleled way to make hazard predictions, as it uses a human dendritic cell line to monitor immunological responses to chemical exposure. This approach offers not only higher predictive accuracy than in vivo tests, but also eliminates the need for animal testing at the same time as it cuts costs and experimental time. The need for ethical, accurate and economical safety assessment procedures is growing in parallel with the amount of new chemicals introduced yearly into our environment by the pharma, cosmetics and chemical industries. In order to protect both workers and customers from chemically induced sensitization, EU legislation dictates that all chemicals must be sensitization tested within the next two decades. The EC and ECVAM have a leading role within the OECD to develop test guidelines for skin sensitization hazard identification. Similar steps have not yet been taken in the area of respiratory sensitization due to the lack of available test methods to identify such compounds. The GARDair assay thus holds the power to drive the EU regulatory framework by entering a globally uncontested market space. If successful, the project will allow SenzaGen to become a key player in any application of animal-free, cell-based systems, aiming to assess sensitization capabilities of various compounds and to fully realize its business development strategy.

Keywords: chemical sensitization,respiratory sensitization,genomic testing technology,biomarker,signature,human dendritic cell line
Topological Crystalline Insulator Nanowires (TOCINA)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Synthetic Chemistry and Materials
Panel description: New materials and new synthetic approaches, structure-properties relations, solid state chemistry, molecular architecture, organic chemistry
Countries involved: Netherlands
Call: ERC Advanced Grant (ERC-2018-ADG, 2018)
Start date: 2019-09-01
End date: 2024-08-31
Duration (months): 60.0
Total project cost (EUR): 2500000.0
EU funding (EUR): 2500000.0
Participants: 1
Project status: SIGNED

The key challenge in quantum computation is decoherence - the collapse of a quantum state due to local perturbations. In this proposal we address this challenge by developing a new nanomaterials system, which forms the core of a future topological quantum computer. In a topological quantum bit, information is encoded in Majorana modes, which are topologically protected by a local symmetry and therefore have long coherence times. In this project we develop a new state of matter -topological crystalline insulator nanowires- in which the topology is defined by the band inversion and the crystal symmetry of the material. Therefore, these topological states should be exceptionally robust. Further, we integrate strong superconductors on these nanowires. These two features together should increase the energy scales of the system compared to current state-of-the-art devices, and therefore lead to stable and electrically-isolated Majorana states. In this project we develop new crystal growth strategies, which enable to grow out-of-thermodynamic equilibrium structures. We will be the first to employ Molecular Beam Epitaxy (MBE) to precisely tune the SnTe nanowire growth conditions. We use the directionality offered by MBE to shadow-grow superconductors on one nanowire facet. The in-situ ultra-high-vacuum growth of hybrid semiconductor/superconductor devices will result in unprecedented device quality. Due to the increased energy scales, experiments, which have been unattainable so far, come within reach. We use this new materials platform to demonstrate entanglement of two Majorana modes at the ends of a nanowire. This quantum teleportation is a groundbreaking experiment and is the key of a topological quantum computer.

Keywords: nanowire,semiconductor,superconductor
Machine Learning for Tailoring Organic Semiconductors (MALTOSE)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Standard European Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: Spain
Call: Individual Fellowships (H2020-MSCA-IF-2019, 2019)
Start date: 2021-03-01
End date: 2024-07-30
Duration (months): 24.0
Total project cost (EUR): 172932.48
EU funding (EUR): 172932.48
Participants: 1
Project status: SIGNED

“Machine Learning for Tailoring Organic Semiconductors” (MALTOSE) connects fundamental materials research with machine-learning (ML) techniques, focusing on the electronic properties of organic semiconductors. The aim of this innovative project is to discover and design novel materials with exciting properties, the prime example being the design of compounds for better organic photovoltaic cells, i.e., that reach higher power-conversion efficiencies and are more stable and more environmentally friendly. The methodology relies on a deep tensor neural network, the so-called PredictNet, that is designed and trained to predict electronic properties of molecules and polymers, at a fraction of the numerical cost compared to density-functional theory (DFT) computations, not to mention experimental measurements. PredictNet will be particularly fruitful in combination with a genetic algorithm that will be developed to propose candidate compounds from crossover and mutation from previously successful compounds. MALTOSE will enable the identification and design of promising compounds, out of the immense pool of imaginable molecules and materials, for future technological applications in fields like organic photovoltaic solar cells, large-area electronic displays, flexible organic electronics, or sensors. The project will bring together the fellow, a recognized quantum physicist and data scientist with academic and industry research experience, and a top research host institution under the supervision of a leading expert in materials science, genetic algorithms, modelling, simulation and knowledge transfer. The fellow will receive an advanced training programme in research skills and complementary non-research-oriented skills in order to enhance his future career prospects and to provide a strong basis for an independent career.

Keywords: organic semiconductors,doping,materials discovery,machine learning,deep learning,genetic,algorithms
Inducing Neural Plasticity Using Electrical Stimulation Delivered by Nano-Structured Electrodes: A Critical Step Toward Post-Stroke Recovery (NeuPES)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Standard European Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Life Sciences
Countries involved: Spain
Call: Individual Fellowships (H2020-MSCA-IF-2017, 2017)
Start date: 2018-11-01
End date: 2020-10-31
Duration (months): 24.0
Total project cost (EUR): 158121.6
EU funding (EUR): 158121.6
Participants: 1
Project status: CLOSED

The main objective of NeuPES is to advance our understanding of the effect of electrical stimulation (ES) on neural plasticity using nanostructured electrodes. The motivation is to develop an effective therapeutic approach that minimizes neural damage and enhances recovery following brain ischemia. Stroke is a second leading cause of disability in Europe. Moreover, stroke treatment does not have a clear clinical pathway with a predictable end point. Acute strokes are treated within a narrow window of time after the ischemic insult by means of endovascular therapies. After the acute period, intensive physical rehabilitation of the affected anatomical region is the primary current therapy. ES of the brain for therapeutic purposes was established more than 50 years ago, and it constitutes a smart approach for inducing neural recovery after stroke. However, there are still large knowledge gaps about the electrode material/design, effective ES regimes, and the underlying mechanisms by which ES triggers different pathways to induce neural plasticity. This project will address these important challenges by bringing together a multidisciplinary team that includes experts in nano-material science, biomedical engineering and neuronal biology. We propose three objectives: (i) fabrication of nano-structured electrodes that maximize the charge injection capacity by increasing the surface area and improve the biocompatibility; (ii) integration of these electrodes to the electro-bioreactor, an in vitro ES setup recently developed by the Researcher with great flexibility to apply different patterns of electrical signals; (iii) use of the electro-bioreactor to explore the effects of different ES regimes on neural plasticity and recovery and investigate the underlying mechanisms to optimize the ES parameters. The multidisciplinary skills acquired during the project will significantly enhance the future career prospects of the Applicant.

Keywords: neural plasticity,electrical stimulation,nano structured electrodes,electro bioreactor,astrocytes
Bringing novel Carbon Nanomembranes to the water filtration market (THINtoBIG)

Framework: Horizon 2020 — Excellent Science
Programme: Future and Emerging Technologies (FET)
Theme: EIC Pathfinder
Destination: Novel ideas for radically new technologies
Action type: Coordination and support actions
Countries involved: Germany,Netherlands,Netherlands
Call: FET Innovation Launchpad (H2020-FETOPEN-2018-2019-2020-4, 2020)
Start date: 2021-05-01
End date: 2022-01-31
Duration (months): 9.0
Total project cost (EUR): 142857.14
EU funding (EUR): 100000.0
Participants: 3
Project status: CLOSED

The project described in this FET Innovation Launchpad proposal aims to bring ultrathin carbon nanomembranes (CNMs) as a gamechanger to the market of demanding water separation applications. It follows-up on the scientific results in the FET Open project ITS-THIN, in which CNM Technologies as a 2D-materials specialist and BLUE-tec as a water treatment process developer participate. Together these companies now wish to join forces to prepare for the production and sales of CNM based composite membranes. The proposed project THINtoBIG results into a conclusive business plan to be able to raise funds and find investors for production, marketing and sales of CNM-composite membranes.

Keywords: membrane science and technology,carbon nanomembranes,water purification,concentration in food and beverage industry,osmosis,business development,membrane production,filtration,separation