Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Japan and Europe Network for Neutrino and Intensity Frontier Experimental Research 2 (JENNIFER2)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Research and innovation staff exchange
Action type: Marie Skłodowska-Curie actions
Panel: Physics
Countries involved: Israel,Austria,Germany,Spain,Italy,Italy,Italy,Switzerland,Türkiye,Poland,Poland,Slovenia,France,Czechia,United Kingdom,United Kingdom,Japan,United Kingdom
Call: Research and Innovation Staff Exchange (H2020-MSCA-RISE-2018, 2018)
Start date: 2019-04-01
End date: 2025-05-31
Duration (months): 74.0
Total project cost (EUR): 2461000.0
EU funding (EUR): 2451800.0
Participants: 20
Project status: SIGNED

JENNIFER2 is the evolution and development of the research and communication activities currently being carried on by the JENNIFER MSCA-RISE project, which will be concluded at the end of march 2019. The new challenges of fundamental physics require to use different complementary approaches and to design experiments able to test different “messengers” of the new physics world. The JENNIFER2 project is actually implementing this requirement, putting together research programs at experimental facilities located in Japan including accelerator produced neutrinos (T2K and HyperK collaborations), cosmic neutrinos detection (HyperK collaboration) and a high luminosity electron-positron collider (Belle II experiment at SUPERKEKB) where very rare processes can be observed. The collaboration of European scientists with the Japanese research community is fostered in all experimental issues, while specific knowledge sharing among different experiments is planned in the field of photon detection, computing, real time and remote controls, data analysis algorithms and theory calculations, aiming to build up real synergies on key technologies and research methodologies, as well as on dissemination and outreach. Such cross-fertilization between different experimental approaches is the crucial step towards an effective multi-messenger approach.

Keywords: flavour physics,physics beyond the standard model,accelerator produced neutrinos,supernova neutrinos,cp violation in the neutrino sector,dark photon search
Fostering a young, creative and inclusive European Space Community (spaceEU)

Framework: Horizon 2020 — Industrial Leadership
Programme: Space
Action type: Coordination and support actions
Countries involved: Ireland,Spain,Austria,Germany,Belgium,Greece,France,Netherlands,Portugal
Call: Space outreach and education (H2020-SPACE-2018, 2018)
Start date: 2018-12-01
End date: 2020-11-30
Duration (months): 24.0
Total project cost (EUR): 993833.13
EU funding (EUR): 993833.13
Participants: 11
Project status: CLOSED

spaceEDU will implement an exciting Space Outreach and Education programme to motivate young people, encourage them to choose space-related careers. spaceEDU will also capture the interest of students towards education paths in the fields of STEAM. spaceEDU will use the perspective of space and the Universe to inspire and broaden young people’s minds, develop a sense of European and global citizenship and build long-term partnerships between people from different cultural backgrounds and countries inside and outside Europe. spaceEDU will also lay the groundwork for the possible future establishment of a Knowledge Innovation Community(KIC) in the field of Space.

Keywords: space and youth,space and society,space and policy makers,space and gender dimension,space and underprivileged communities,space and kics
The origins of thermonuclear supernova explosions (SNeX)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Universe sciences
Panel description: Astro-physics/-chemistry/-biology; solar system; planetary systems; stellar, galactic and extragalactic astronomy; cosmology; space sciences; astronomical instrumentation and data
Countries involved: Israel
Call: ERC Consolidator Grant (ERC-2019-COG, 2019)
Start date: 2020-08-01
End date: 2025-07-31
Duration (months): 60.0
Total project cost (EUR): 2000000.0
EU funding (EUR): 2000000.0
Participants: 1
Project status: SIGNED

Type-Ia supernovae (SNe) are thought to originate from thermonuclear explosions of carbon-oxygen (CO) white-dwarfs (WDs). They play a key role in the evolution of the universe (producing most of the Iron-peak elements); and serve as critical cosmological distance-indicators. The main proposed SNe progenitors are CO-WDs accreting material from stellar companions; and mergers of two CO-WDs. However, all suggested models fail to reproduce the diverse physical characteristics of Ia-SNe; their inferred rates/ages/luminosity distrbution; and their puzzling wide sub-types diversity. Finding the origins and the evolutionary pathways of thermonuclear SNe remains one of the most important “holy grail” open questions in modern astronomy. Here we propose novel directions and potential solutions to this question, and suggest new scenarios for the origin of all sub-types of thermonucelar SNe. Supported by preliminary results, we propose that (1) the little-explored mergers of CO-WDs with hybrid He-CO WDs play a key-role in producing most types of SNe, and may provide a viable model for the origin of the majority of thermonuclear SNe, their diversity and their distrbutions; (2) neutron star-WD mergers may explain the origin of peculiar rapidly evolving SNe; (3) the channel of exploding accretion-grown massive CO-WDs never/rarely gives rise to standard Ia-SNe. We propose an end-to-end open-source-based modelling of SNe (providing easy access and reproducibiliy of our results) including stellar evolution of their progenitors; 3D hydro simulations of WD mergers; 2D (+3D) thermonuclear-hydrodynamical+radiative-transfer models (predicting detailed light-curve/spectra/compostion observables); and population synthesis studies. Our proposed science can potentially transform the field; solve the century-long puzzle of Ia-SNe and explain their origins; and provide critical input for understanding the evolution of the universe and the measurements of its fundamental cosmological parameters.

Keywords: supernova,thermonuclear,nucleosynthesis,stellar evolution,white dwarfs,neutros stars,binary stars,standard candles,type ia supernova,iax supernova,type ib supernova,light curve,spectra,chemical evolution of galaxies
Partnership formation in the context of recent refugee migration (PARFORM)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: The Social World and Its Diversity
Panel description: Sociology, social psychology, social anthropology, education sciences, communication studies
Countries involved: Germany,Germany
Call: ERC Consolidator Grant (ERC-2019-COG, 2019)
Start date: 2021-03-01
End date: 2026-02-28
Duration (months): 60.0
Total project cost (EUR): 1999998.0
EU funding (EUR): 1087485.25
Participants: 2
Project status: SIGNED

In 2015-16 Germany received large numbers of young unmarried male refugees arriving from culturally distant countries and settling in largely non-existent co-ethnic communities. This situation is likely to complicate the process of refugees’ partnership formation. Against this background the project seeks to answer three research questions: 1. To what extent is the partnership formation of newly arrived refugees in Germany determined by the demographic situation, and how does the process compare to patterns observed among the established German population (both majority and minority ethnic groups)? 2. How do social media and online dating affect partnership formation among recent refugee immigrants? 3. How do established members of German society react to refugees’ mating attempts, and how is this reflected in immigrants’ partnership patterns? Whereas the first research question is largely descriptive, the others seek to advance the field’s theoretical and analytic frameworks by extending our understanding of opportunity structures in the age of the internet and social media, and conceptualizing partnership formation as a genuinely two-sided interactive process. PARFORM will (1) collect novel three-wave panel data on male refugee migrants from Syria and Afghanistan and (2) administer specially tailored instruments to two established representative panel studies (GESIS panel and CILS4EU-DE) to collect data on the established German population. Multi-actor, multi-time and multi-level data will be analysed using advanced methods of network, panel and hierarchical data analyses. Through a combination of theoretical innovations, methodological rigour and analytic depth, PARFORM will contribute to scholarly debates on immigrants’ partnership formation in the multidisciplinary fields of the sociology of migration, demography and network studies. Its findings will have policy implications for the social integration of refugees in Germany and Europe as a whole.

Keywords: refugee migrants,partnership formation,social integration
Game changer in high temperature steam electrolysers with novel tubular cells and stacks geometry for pressurized hydrogen production (GAMER)

Framework: Horizon 2020 — Societal Challenges
Programme: Secure, clean and efficient energy
Theme: Public-private partnerships
Destination: Fuel Cells and Hydrogen
Action type: Research and Innovation actions
Partnership: FCH2 (Art-187)
Countries involved: Spain,Iceland,Spain,Norway,Norway,Netherlands
Call: Game changer High Temperature Steam Electrolysers (H2020-JTI-FCH-2017-1, 2017)
Start date: 2018-01-01
End date: 2022-09-30
Duration (months): 57.0
Total project cost (EUR): 2998951.25
EU funding (EUR): 2998951.25
Participants: 8
Project status: CLOSED

The GAMER project will develop a novel cost-effective tubular Proton Ceramic Electrolyser (PCE) stack technology integrated in a steam electrolyser system to produce pure dry pressurized hydrogen. The electrolyser system will be thermally coupled to renewable or waste heat sources in industrial plants to achieve higher AC electric efficiency and efficient heat valorisation by the integrated processes. The project will establish high volume production of the novel tubular proton conducting ceramic cells. The cells will be qualified for pressurized steam electrolysis operation at intermediate temperature (500-700°C). They will be bundled in innovative single engineering units (SEU) encased in tubular steel shells, a modular technology, amenable to various industrial scales. GAMER will develop designs of system and balance of plant components supported by advanced modelling and simulation work, flowsheets of integrated processes, combined with robust engineering routes for demonstrating efficient thermal and electrical integration in a 10 kW electrolyser system delivering pure hydrogen at minimum 30 bars outlet pressure. The consortium covers the full value chain of the hydrogen economy, from cell and SEU manufacturer (CMS), system integrators (MC2, CRI), through researchers (SINTEF, UiO, CSIC), to end users in refineries, oil and gas, chemical industry (CRI, Shell with advisory board members YARA and AirLiquide). All along the project, these experienced partners will pay particular attention to risk management (technical, economic, logistic, business) and ensure progress of the technology from TRL3 to TRL5. The overall consortium will perform strategic communication with the relevant stakeholders in order to ensure strong exploitation of the project’s results.

Keywords: electrolysers,hydrogen production,high temperature,proton ceramic electrolyser
Engineering Scalable Algorithms for the Basic Toolbox ScAlBox (ScAlBox)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Computer science and informatics
Panel description: Informatics and information systems, computer science, scientific computing, intelligent systems
Countries involved: Germany
Call: ERC Advanced Grant (ERC-2019-ADG, 2019)
Start date: 2020-09-01
End date: 2025-08-31
Duration (months): 60.0
Total project cost (EUR): 2498785.0
EU funding (EUR): 2498785.0
Participants: 1
Project status: SIGNED

ScAlBox aims at basic algorithmic tools that can be used in a wide spectrum of applications and that scale orders of magnitude better than the state of the art with respect to input size or number of processors. In the last decades, we witness the transition into the information age with profound effects on science, technology, and our daily life. This transition is driven by a growing spectrum of computer applications that process larger and larger data sets using increasingly complex algorithms. However, the scalability challenge has emerged as a major road block to this progress: An explosion of the amount of data to be processed (big data) coincides with stagnating performance of a single processor core. This widening performance gap can only be closed using many parallel processors. However, parallel algorithms have long been neglected by algorithm theory while heuristic software development optimizes for existing machines and inputs but fails to give predictable scalability for future, larger data sets and more processors. We want to overcome this roadblock by developing scalable solutions for the basic toolbox of algorithms and data structures that are needed in many applications (e.g., sorting, searching, queues, basic graph algorithms, collective communication, and load balancing). My goal is to provide algorithms and software libraries that scale to millions of processors and give hard performance guarantees for arbitrary inputs. This is challenging due to large gaps between theory and practice and because such algorithms have to integrate scalable fault tolerance and dynamic load balancing to an unprecedented extent. I am the right person to achieve this due to my extensive experience in parallel algorithms for irregular problems and my leading role in algorithm engineering that integrates modeling, design, theoretical analysis, implementation, and experimental evaluation.

Keywords: algorithm engineering,parallel algorithms,distributed memory algorithms,graph algorithms,data structures,sorting algorithms,load balancing,memory hierarchies,high performance computing
Real-World Evidence (RWE) analytics to increase efficiency of clinical product development (RWEal)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Finland
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2019)
Start date: 2019-08-01
End date: 2019-12-31
Duration (months): 5.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

Medical product development is a lengthy and expensive process with limited success. After more than €40 million and over 10 years for clinical programmes, only 15% of new therapies reach the market. Many of the new therapies fail at the post-marketing phase due to unexpected side effects or insufficient efficacy. This is mainly because clinical trials focus on a narrow group of patients that do not represent the general patient population. Pharmaceutical and MedTech industries urgently need novel solutions to increase the efficiency and generalizability of clinical research programs. Benete is an information technology-oriented Finnish company that developed a Life Analytics Platform to provide high-quality, accurate, and reliable Real-World Evidence (RWE) to clinical trials. The Life Analytics Platform collects a novel source of real-life data to generate behaviour and functionality models of patients, combining ambient sensors with advanced artificial intelligence technology. Benete’s technology observes patients in their daily routines and identifies changes in their functioning in real life. This game-changing approach will provide medical product developers with invaluable insights into the efficacy and safety of a medical product in diverse patient populations, outside of the controlled environment of the clinic. The RWE data provided by Benete’s service will directly benefit clinical product development by 1- accelerating the timelines, 2- decreasing the overall costs, 3- increasing the predictive power, 4- supporting regulatory and reimbursement processes. Benete will use this SMEi phase1 funding to 1) assess the market requirements of its Life Analytics Platform by conducting a comprehensive market analysis and user involvement study, 2) identify strategic partners, and 3) establish IP and regulatory strategies. These activities will allow Benete to define the best commercialisation strategy for the Life Analytics Platform in the clinical trial market.

Keywords: real world data,clinical trial efficiency,post market surveillance,life cycle management,sensor,deep learning,patient monitoring,value based outcomes,value based healthcare,dose optimisation
Smart Cities EMC Network for Training (SCENT)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Innovative Training Networks (ITN)-European Joint Doctorates
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: Poland,Poland,Poland,Poland,Poland,United States,Netherlands,Netherlands,Netherlands,Netherlands,United Kingdom,United Kingdom,United Kingdom,United Kingdom,United Kingdom
Call: Innovative Training Networks (H2020-MSCA-ITN-2018, 2018)
Start date: 2018-09-01
End date: 2022-08-31
Duration (months): 48.0
Total project cost (EUR): 2388813.12
EU funding (EUR): 2388813.12
Participants: 17
Project status: CLOSED

The global vision of Smart and Sustainable Cities is greatly restricted by the rapid increase of interference and interoperability problems which occur through the interaction of electrical power with information technology and communications equipment. This interconnected systems-of-systems infrastructure creates a complex electromagnetic environment in which interoperability of the electronic systems has to be achieved. There is an urgent need to conduct greater understanding in Power Quality (PQ) and (conducted) Electromagnetic Compatibility (EMC) due to the changing dynamics of the field. The main issue is the insufficient number of qualified engineers for the latest developments in smart cities, renewable generation and electric vehicles. A highly trained cadre of engineers (a new breed) is required to lead this area. To date no specific doctoral programme in this field is available in Europe resulting in well trained researchers and engineers being scarce. SCENT will create a network to train highly skilled engineers through an integrated doctoral training program based on the essential research required in PQ and EMC that can underpin all future technological developments, strongly linked to industry, and bridging the gap between research and industry for translation of new knowledge. Designed into the project are specific innovations with methodologies to optimize the design of power distribution networks inside buildings and industrial plants, as well as transport systems, with respect to compatibility (no interference) and efficiency in the distribution networks and connected electronic systems. The partners of the network consist of leaders in these fields and relevant industrial stakeholders resulting in a unique and effective network in Europe and globally. Dissemination methods to realize optimal impact will include scientific publications, workshops, training of engineers in industry, and dissemination through newsletters, interviews, social media.

Keywords: interference
Precise and Robust Navigation enabling Applications in Disturbed Signal Environments (PARADISE)

Framework: Horizon 2020 — Industrial Leadership
Programme: Space
Action type: Innovation actions
Countries involved: Austria,Austria,Germany,Germany,Czechia
Call: Small and Medium Enterprise (SME) based EGNSS applications (H2020-Galileo-2014-1, 2014)
Start date: 2015-01-01
End date: 2017-07-31
Duration (months): 31.0
Total project cost (EUR): 1157272.5
EU funding (EUR): 838590.0
Participants: 5
Project status: CLOSED

Precise and Robust Navigation enabling Applications in Disturbed Signal Environments (PARADISE). Enabling precise and robust satellite navigation not only under open sky conditions but also under disturbed GNSS signal conditions (typically in forest and in urban canyon environments) is the last open challenge for precise satellite navigation in outdoor. Concerning accuracy, the current existing 'state-of-the-art' professional GNSS receivers are enabling precise GNSS positioning solutions with an accuracy better than 0,5 m. These receivers are only working under open sky (strong signal without major multipath effects) conditions. There are many GNSS receiver working under disturbed signal conditions (mass market receivers), but these are typically providing accuracies of 10-30 meters in disturbed environments and are therefore not suited for applications requiring high accuracy in disturbed signal environments. Concerning robustness, often an integration with medium performance inertial sensor is done to overcome short periods of disturbance. However if working in a generally disturbed environment is the nominal case, this approach doesn't help any further. Another drawback of existing IMU integration solutions is the high costs related with the need of ahigh quality IMU sensor (from 1.000 to 10.000 EUR). To overcome these limitations, IFEN is working since many years on this problem. With projects finalized in February 2013, a first receiver bread-board is existing, confirming the viability of the proposed technical concept (called micro-trajectory augmented with MEMS sensors) at a TRL3. Extending the current concept towards multi-frequency (GPS L1/L5 and Galileo E1 and E5) and inserting a low-cost MEMS (below 100 EUR) will further increase accuracy and robustness, making it suited to be transfered towards a marketable technology. The marketable prototype receiver (TRL7) will be demonstrated in typical precise applications in forest and urban environments.

Keywords: high accuracy robustness weak signals forest urban applications