Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Mobilization of innovative design tools for refurbishing of buildings at district level (MODER)

Framework: Horizon 2020 — Industrial Leadership
Programme: Nanotechnologies, Advanced Materials, Advanced Manufacturing and Processing, and Biotechnology
Theme: Technologies enabling energy-efficient systems and energy-efficient buildings with a low environmental impact
Action type: Innovation actions
Countries involved: Germany,Austria,Finland,Slovenia,Finland,Slovenia,Latvia,Netherlands
Call: Innovative design tools for refurbishing of buildings at district level (H2020-EeB-2015, 2015)
Start date: 2015-09-01
End date: 2018-08-31
Duration (months): 36.0
Total project cost (EUR): 4078885.0
EU funding (EUR): 3280582.0
Participants: 10
Project status: CLOSED

MODER vision is cost-effective, energy-efficient and user-oriented refurbishment of buildings as part of the global energy system. The main objective of MODER is to increase business of engineering companies, energy managers and consultants in supporting municipalities and building owners in European and global markets for the refurbishment of buildings at district level. The project will develop, demonstrate and mobilize design tools, processes and business models for efficient refurbishment on building and district level. Design tools will enable evaluation of different retrofitting alternatives at different stages of projects, consideration of district level impacts and simultaneous consideration of cost and energy efficiency. Process development aims at sustainable retrofit design especially by developing models for procurement and models for design management. Business models focus on the generation and delivery of distributed energy to support new retrofit solutions which enable the use of local renewable energy sources. MODER consortium consists of 10 partners including 2 large engineering companies, 2 research and technology organisations and 6 SMEs of which one is a public energy manager. All partners are aiming at developing their engineering and / or consultation business towards refurbishment of buildings at district level. 4 partners aim at reinforcing their engineering or consultation business in areas outside Europe.

Keywords: energfy efficiency,renewable energy,refurbishment,district,design tools,process,business models
Spintronic-Photonic Integrated Circuit platform for novel Electronics (SPICE)

Framework: Horizon 2020 — Excellent Science
Programme: Future and Emerging Technologies (FET)
Theme: EIC Pathfinder
Destination: Novel ideas for radically new technologies
Action type: Research and Innovation actions
Countries involved: Belgium,France,Netherlands,Denmark,Denmark
Call: FET-Open research projects (H2020-FETOPEN-2014-2015-RIA, 2015)
Start date: 2016-10-01
End date: 2020-12-31
Duration (months): 51.0
Total project cost (EUR): 3395178.25
EU funding (EUR): 3395178.25
Participants: 6
Project status: CLOSED

The main objective of SPICE is to realize a novel integration platform that combines photonic, magnetic and electronic components. Its validity will be shown by a conceptually new spintronic-photonic memory chip demonstrator with 3 orders of magnitude higher write speed and 2 orders of magnitude lower energy consumption than state-of-the-art spintronic memory technologies. This enables, e.g., future petabit-per-second processor-memory bandwidths, required in a decade from now, and highly energy-efficient exascale datacenters with reduced carbon footprint. Such a versatile memory will result in so-called Universal Memory: one technology for all memory applications ranging from cache to storage. The methods to achieve this are based on the recent discovery of magnetization reversal by short optical pulses. SPICE will bring this technique to the integrated circuit level by first developing free magnetic layers that can be optically switched into a magnetic-tunnel-junction layerstack, with optically transparent top contacts. These layers will then be processed into spintronic memory elements that can be electrically read. A novel short-pulse switching architecture will be designed and implemented in a silicon photonic integrated circuit. This photonic switching layer will then be combined with the spintronic memory layer to achieve an optically switched 8-bit memory with write efficiency of 600 fJ per bit: the proof of concept of the technology. The novelty of SPICE is the convergence of the emerging fields of opto-magnetism and spintronics with electronic and photonic integration technologies. The ambition is to develop this technology in such a way that it can be compatible with future mature electronics fabrication processes, for real-world applications beyond 2025, thereby creating a new field. The SPICE platform is therefore foundational as it can be used not only for ultrafast and energy-efficient memory, but also for RF nano-oscillators and sensor technology.

Keywords: spintronics,photonics,opto magnetism,spin transfer torque memory,silicon photonics,non volatile memory
A REVOLUTIONARY SYSTEM IN THE EMERGING MARKET OF ADVANCED ANALYTICS. Graphext bridges the gap between Data Science and Business Intelligence, democratising Advanced Analytics. (Graphext)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Spain
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2018)
Start date: 2018-06-01
End date: 2018-11-30
Duration (months): 6.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

The landscape for data analytics is undergoing profound changes, 1) because of the growing awareness of organisations in the intrinsic value of the data, 2) because of the huge explosion in amount and sources of data accessible, and 3) because of developments in tools and methods to extract value from the data. The challenge for organisations is to optimally extract intrinsic value (economic, social and environmental) to remain effective and competitive. Organisations that can achieve this rapidly and most economically will certainly excel. Currently, the tools available on the market have are scattered and diverse, on one hand being conventional BI that have failed to keep up (with traditional databases and visualisation schema designed for structured data), and on the other there are sophisticated tools designed for use by data scientists, creating a major gap in between, to reach business analysts and decision makers. To date there are no tools on the market that combine these advanced data science methods together with user-friendly value-creation results and front-ends, aimed at tactical and strategic decision making. Graphext disrupts the existing market for data-science and BI analytics, by combining the full-stack functionalities in one seamless cloud-based SaaS solution. Graphext’s revolutionary approach in the analysis and visualization of complex datasets, brings the field of advanced Data Science to the mainstream. Graphext delivers an advanced system for data analytics and value-creation - providing direct ‘actionable insight` for decision makers. Graphext applies advanced scientific techniques with unsupervised automated learning together with advanced data visualisation. This is an entirely different approach to data analysis as it takes a network and statistical approach, e.g. through employing techniques such as heuristics, looking at inherent patterns, anomalies and extracting intrinsic meaning from the data, doing the work of the data scientists.

Keywords: advanced analytics,data science,business intelligence
Ultimate Paleo-Ocean Records from Biogenic Calcites (UltraPal)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Earth system science
Panel description: Physical geography, geology, geophysics, atmospheric sciences, oceanography, climatology, cryology, ecology, global environmental change, biogeochemical cycles, natural resources management
Countries involved: Switzerland
Call: ERC Advanced Grant (ERC-2017-ADG, 2017)
Start date: 2019-01-01
End date: 2024-12-31
Duration (months): 72.0
Total project cost (EUR): 2435000.0
EU funding (EUR): 2435000.0
Participants: 1
Project status: SIGNED

The ambition with this proposal is to create ultimate paleo-environmental records for the oceans. This is a fundamental scientific challenge and the motivation is simple: The oceans take up 70% of the Earth’s surface area and represent an immense sink/source of, e.g., heat and CO2, which makes them key to the evolution of Earth’s climate. Assessing and understanding anthropogenic global climate change requires that role of the oceans is understood in detail. Proxies are needed to establish past ocean conditions with high accuracy. Calcite has played a fundamental role in these efforts because isotopic and trace element compositions of limestone and calcitic fossils are related to ocean conditions at the time of their formation. Geochemical studies of calcite in the ocean sediment record have therefore contributed enormously to the understanding of Earth’s climatic evolution over the last several hundred million years. However, our recent work (Bernard et al., Nature Communications, 2017) identifies a fundamental problem: Visually imperceptible, ultrastructure-level processes that occur during sediment diagenesis can introduce a very strong bias in these records, in particular those based on biogenic calcite; i.e., structures produced by living organisms such as foraminifera and brachiopods. Previously not investigated or taken into account, such ultrastructure-level diagenesis will (and does) create large errors in ocean paleo-environmental reconstructions, even under the close-to-ambient pressure and temperature conditions characterizing shallow sediment burial. This proposal offers a solution: An entirely new, interdisciplinary approach, including ultra-high-resolution isotopic imaging (NanoSIMS), is developed here to quantify these effects in a broad range of biogenic calcites, permitting genuinely non-biased, calcite-based paleo-ocean reconstructions to be created. The impact of this work on climate change research will be dramatic and immediate.

Keywords: biogenic calcite,diagenesis,diffusion,pseudomorphic replacement,coupled dissolution and reprecipitation,stable isotopes,nanosims,fossils,ultrastructure,sediments,foraminifera,paleo ocean
Research on Microeconometrics: Identification, Inference, and Applications (ROMIA)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Individuals, Markets and Organisations
Panel description: Economics, finance and management
Countries involved: United Kingdom
Call: ERC Consolidator Grant (ERC-2014-CoG, 2014)
Start date: 2016-01-01
End date: 2021-12-31
Duration (months): 72.0
Total project cost (EUR): 1292297.0
EU funding (EUR): 1292297.0
Participants: 1
Project status: CLOSED

This research project is motivated from three observations regarding recent trends in empirical economics using micro-level data. First, researchers are increasingly aware of the trade-off between credibility and the strength of the assumptions maintained, eloquently termed as the law of decreasing credibility by Charles F. Manski. This trend has led to recent intensive research in partial identification. Second, applied empirical research is increasingly based on data collected for study by individual researchers, quite often through laboratory or field experiments. Third, high-dimensional data are more readily available than ever before, and have received growing attention in economics. In view of these observations, there is a call for research to improve standard econometric practice by facing identification problems upfront, by providing econometrically sound guidelines for data collection, and by making use of the increasing availability of high-dimensional data without sacrificing the credibility of econometric methods. This research project aims to contribute to advances in microeconometrics by considering the issues of identification, data collection, and high-dimensional data carefully. The proposed research builds on semiparametric and nonparametric approaches to increase the credibility of proposed econometric methods. In particular, the proposed research will: (1) develop identification results of practical value and characterize optimal data collection for applied researchers; (2) make advances in estimation, inference, and testing in a variety of microeconometric models; (3) produce credible evidence in applied microeconometric research; (4) develop computer software that implements newly available microeconometric techniques.
Optimizing Manufacturing Processes through Artificial Intelligence and Virtualization (OPTIMAI)

Framework: Horizon 2020 — Industrial Leadership
Programme: Nanotechnologies, Advanced Materials, Advanced Manufacturing and Processing, and Biotechnology
Theme: Technologies for Factories of the Future
Action type: Innovation actions
Countries involved: Spain,Spain,Germany,Spain,Italy,Spain,Ireland,Ireland,Italy,Cyprus,Greece,Greece,Greece,Finland,Greece,United Kingdom
Call: Quality control in smart manufacturing (IA) (H2020-NMBP-TR-IND-2020-singlestage, 2020)
Start date: 2021-01-01
End date: 2023-12-31
Duration (months): 36.0
Total project cost (EUR): 10453125.0
EU funding (EUR): 8359087.5
Participants: 16
Project status: SIGNED

There is a common saying in industry, and more generally in engineering "fast, cheap, reliable, choose two out of three". This saying captures the trade-off between resources, production time, quality and performance that are inherent in every manufacturing process. Conceptually, different points on this "Paretto front" correspond to different production choices and directly affect the competitiveness of an industry. OPTIMAI aims to redefine and optimize this Pareto front through integrating several enabling technologies in a common framework. The starting point for OPTIMAI is the smart instrumentation of production with AI-enabled sensors for quality inspection and monitoring, integrated on a secure middleware layer. To ensure data integrity and traceability, OPTIMAI foresees distributed ledger technology combined with a cyber-security module. Collected data are analysed using AI models for the early detection of defects and upstream causes of deficiencies. OPTIMAI also explores the virtualization of production using digital twins of processes and sensors that combined with AI models trained on production data, form a simulation engine for exploring production scenarios and optimizing production planning. Another innovative point is the rapid reconfiguration of production equipment via automated feedback from quality control results or via a context aware Augmented Reality ecosystem for fast and informed decision making. At the same time OPTIMAI put emphasis on establishing a comprehensive ethics and regulatory framework for the deployment of its technology realizing that a production line is a workplace and as such concerns related to privacy, protection of human rights and safety have to be carefully addressed before any kind of technology is adopted. OPTIMAI foresees an extended pilot phase on three industrial sites covering a representative sample of European industry in order to maximize its impact and facilitate commercial uptake.

Keywords: ai enabled instrumentation,secure sensor network,artificial intelligence,virtualization,digital twins,augmented reality
Operando FTIR spectro-electrochemistry of hydrogenases: unraveling the basis of biological H2 production for innovative clean energy technologies (H2Bio2Energy)

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: Chemistry
Countries involved: United Kingdom
Call: Individual Fellowships (H2020-MSCA-IF-2016, 2016)
Start date: 2018-01-08
End date: 2020-01-07
Duration (months): 24.0
Total project cost (EUR): 183454.8
EU funding (EUR): 183454.8
Participants: 1
Project status: CLOSED

Ongoing search for renewable energy technologies is essential in Europe to tackle the continuous increase in demand and the environmental and ethical concerns. Hydrogen is a promising renewable fuel, but so far its production is not clean as the conventional industrial techniques start from fossil fuels. Production of H2 by biological, biotechnological or bioinspired methods would solve this problem and the study of FeFe-hydrogenases is a key step in this process. For this reason, the proposal is well aligned with the EU Societal Challenges in “Secure, clean and efficient energy”. FeFe-hydrogenases are efficient metalloenzymes that catalyse H2 production from water at high turnover rates, using Fe, rather than Pt active sites. Building on my previous experience with these enzymes, in this project I will apply a highly innovative approach to investigate how nature uses cheap iron to catalyse H2 production. Protein film infrared (IR) electrochemistry was recently developed by Prof Vincent at the University of Oxford, allowing electrochemical triggering of enzyme catalysis with simultaneous IR spectroscopic study of how the enzyme works. This will provide a deep understanding of enzyme structure/function relationships, from coordinated electron and proton transfer to structural rearrangements at the Fe site. Due to my prior expertise in FeFe hydrogenases which are not studied in the host group, and Oxford’s excellence in bio-spectroscopy, the Fellowship will promote exceptional bidirectional knowledge transfer. Dissemination of research through Oxford’s outreach programs will increase the impact of the project to varied audiences. Access to the well-established training and career development activities in Oxford, and the world-class programs of visiting speakers, will advance my career as I gain new knowledge and skills, enhance my scientific profile by acquiring international experience, expand my scientific horizon, strengthen my skills, and build new networks.

Keywords: enzymes,hydrogenases,biohydrogen
Investigating metal-organic frameworks using excited-state dynamics and theoretical spectroscopy (MOFdynamics)

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: Chemistry
Countries involved: Switzerland
Call: Individual Fellowships (H2020-MSCA-IF-2017, 2017)
Start date: 2018-09-01
End date: 2021-03-02
Duration (months): 24.0
Total project cost (EUR): 187419.6
EU funding (EUR): 187419.6
Participants: 1
Project status: CLOSED

The success of metal-organic frameworks (MOFs) is rooted in their structural flexibility enabling limitless possibilities for topology design and applicability. An exhaustive synthesis for potential candidate screening is however tedious and inefficient with regard to material consumption and human resources. Theoretical approaches have thus become indispensable for the simulation and prediction of electronic and structural properties. With the project ''MOFdynamics'', we will make a contribution to theoretical structure modeling which will complement experimental research on MOF design. Our proposed computational approach will enable to perform excited-state molecular dynamics simulations and to calculate X-ray absorption as well as fluorescence spectra for solid-state materials, in particular MOFs. Its innovative capacity and the anticipated impact originate from combining the essential ingredients for efficient structure modeling: The treatment of the porous solids is based on periodic boundary conditions. Density functional theory in combination with hybrid functionals is employed for the description of excited states representing the most adequate electronic-structure model. Large-scale applications are made feasible by exploiting advanced computational and technical procedures for high-performance computing. To this concern, the novel developments will be implemented in the CP2K program package and applied for efficient structural characterization and analysis of experimentally synthesized MOFs. ''MOFdynamics'' will advance computational tools for state-of-the-art research within the interdisciplinary field of quantum chemistry, solid-state physics and materials science. Its contributions will pave the way for a comprehensive theoretical treatment of recent progress in X-ray spectroscopy. Being implemented in a freely available software package, the proposed research represents a significant added value to the community and European excellence.

Keywords: metal organic frameworks,density functional theory,excited states,periodic boundary conditions,x ray spectroscopy
A breakthrough cancer diagnostic instrument to save 3000 years of human lives annually and up (CytoPro)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Austria,Denmark
Call: SME instrument (H2020-SMEInst-2018-2020-2, 2018)
Start date: 2018-11-01
End date: 2021-06-30
Duration (months): 32.0
Total project cost (EUR): 2445232.5
EU funding (EUR): 1711662.75
Participants: 2
Project status: CLOSED

In Europe, approximately 1 in 2 people will get cancer in their lifetimes. State-of-the-art cancer treatments can be tailored to the genetic make-up of a patient’s tumour, making them highly effective at fighting cancer. However, these very expensive treatments, which cost the society €1.5bn each year, can become ineffective over time due to genetic mutations in the tumours. Currently, there is no diagnostic tool available to regularly monitor for such mutations. CytoTrack has developed the CT11™, a unique blood test that helps oncologists to detect and monitor tumour mutations, enabling them to select the right treatment at the right time. The CT11™ can rapidly identify and isolate Circulating Tumour Cells (CTCs) in the bloodstream to provide up-to-date genetic information without invasive biopsies. Whilst our technology has been proven in the research environment, this CytoPro project will refine, demonstrate and clinically validate the CT11™ for use in metastatic Colorectal Cancer (mCRC) and prepare the device for clinical market introduction. A successful project will make the CT11™ the first next-generation cancer blood test on the clinical market. It will allow oncologists to optimise therapies more quickly in response to tumour mutations, extending survival rates up to half a year, avoiding unnecessary side-effects and increasing cost effectiveness of the treatment by 10-20%, which could save €15,000 per treatment. The market for CTC technology is expected to reach €7.8 billion by 2020 and, as the first clinically approved device of its type, we expect the CT11™ to generate rapid business growth for CytoTrack, resulting in an annual revenue of €41m and a profit of €13m by 2026. The team assembled for CytoPro has an enviable track record in commercialising medical innovations and possesses all the skills and resources to successfully deliver the clinical trials and effectively exploit the results for the benefit of our business and for Europe.

Keywords: cancer,diagnostics,ctc circulating tumour cells,liquid biopsy,blood analysis,personalized,medicine,treatment,health care