Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Optimal SIC substR ates for Integrated Microwave and Power CircuitS (OSIRIS)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Theme: Public-private partnerships
Destination: Electronic Components and Systems
Action type: Research and Innovation actions
Partnership: ECSEL (Art-187)
Countries involved: Slovakia,Sweden,Norway,Sweden,France,France,France,France
Call: ECSEL Key Applications and Essential Technologies (RIA) (ECSEL-2014-1, 2014)
Start date: 2015-05-01
End date: 2018-11-30
Duration (months): 43.0
Total project cost (EUR): 4487117.5
EU funding (EUR): 1819212.0
Participants: 10
Project status: CLOSED

OSIRIS project, a Research and Innovation Action (RIA), aims at improving substantially the cost effectiveness and performance of gallium nitride (GaN) based millimetre wave components. The project proposes to elaborate innovative SiC material using isotopic sources. This material will offer thermal conductivity improvement of 30% which is important for devices dissipating a lot of power, in particular in SiC power electronics and in microwave device using GaN high electron mobility transistors (HEMT) grown on SiC semi-insulating substrates. OSIRIS project will allow reinforcing GaN technology penetration into the market by cost effectiveness of the SiC substrates and circuit performances improvement thanks to better heat spreading close to the dissipative area. For microwave GaN/SiC HEMT this isotopic approach could create a complete shift in the currently used substrate / GaN epi-wafer technology; it intends to grow high thermal conductivity (+30%) semi-insulating SiC on top of low cost semiconducting SiC substrates (widely used by the power electronics and LED industries). Reduced layer thickness is necessary as only the top 50 to 100µm SiC wafer is really useful as the substrate itself is currently thinned to realise microstrip waveguided microwave circuits. For power electronics, this isotopic innovation will be essentially focused on thermal improvement, i.e. better electron mobility at a given power dissipation as mobility and drift mobility decrease with temperature and also better carrier transport thanks to lower scattering rates. Schottky and p-i-n diodes will be tested using this material, which however will have to be doped while microwave devices need semi-insulating materials. The improved thermal SiC properties will be obtained by using single isotopic atoms for silicon and carbon, namely 28Si and 12C. The SiC wafer size will be targeted to 100mm (4-inches) which is today widely used on industry.

Keywords: power electronics,microwave electronics,high thermal conductivity substrates,silicon carbide,gallium nitride,material growth,thermal management,mobile communications
Nonlinear Compensation in Hybrid Raman/EDFA Amplified Optical Systems (CHAOS)

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: Marie Skłodowska-Curie Individual Fellowships (IF-EF) (H2020-MSCA-IF-2014, 2014)
Start date: 2015-04-01
End date: 2017-03-31
Duration (months): 24.0
Total project cost (EUR): 158121.6
EU funding (EUR): 158121.6
Participants: 1
Project status: CLOSED

Over the past 25 years, optical fiber communications have heralded a revolution in the high-speed transmission information of information accros the globe, in perfect synergy with the advent of world-changing concepts such as the world-wide-web, big data transmission and processing, video communication or high-definition on-demand entertainment. The capacity of fiber optic communication systems has experienced a steady growth over the years, adapting to society's requirements. In recent years, worries have arisen regarding the theoretical capacity limits for fiber-optic technology, and several methods have been proposed to overcome the barrier posed by the nonlinear Shannon limit. The ambition of this proposal is to combine several of the most promising (both in terms of potential cost and performance) solutions to this problem, developing new methods for nonlinear effect compensation in systems with advanced optical amplification relying on the most efficient transmission formats. This fellowship is to provide the early career, but experienced researcher Pawel Rosa with an opportunity to expand his experimental knowledge in the field of optical communication and combine techniques from digital signal processing and numerical modelling into the development of novel solutions and patentable methods for nonlinearity compensation in optically amplified optical transmission systems with amplification based on either Erbium-doped fibers (EDFA), advanced distributed Raman schemes or a combination of both (hybrid amplification). The multidisciplinary aspects of the proposal will benefit the fellow in his way to reaching professional maturity in the field of optical communications and photonics. The project will blend the concepts of advanced optical amplification, coherent transmission, applied mathematics and novel computational methods, under the supervision of world-leading experts in the corresponding areas.

Keywords: coherent detection,optical communication systems
Efficient Numerical Modeling of Moving Contact Lines under Non-isothermal Conditions (ENMMCL)

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: United Kingdom
Call: Marie Skłodowska-Curie Individual Fellowships (IF-EF) (H2020-MSCA-IF-2014, 2014)
Start date: 2015-10-06
End date: 2017-10-05
Duration (months): 24.0
Total project cost (EUR): 195454.8
EU funding (EUR): 195454.8
Participants: 1
Project status: CLOSED

A moving contact line (MCL) is a moving line of intersection between a fluid/fluid interface and a solid wall. MCLs are central to a wide range of flows in nature and industry, however, their modeling has been a classical difficulty, especially under non-isothermal conditions. The project will tackle this challenge and we will develop a novel computational model enabling simulations of non-isothermal flows involving MCLs with unprecedented efficiency. The model borrows the idea from the large eddy simulation in turbulence modeling; it will resolve the macroscale flows only while model the effect of MCLs using non-isothermal hydrodynamic theories, which will also be developed in the present project. We expect that the model can lead to a reduction of computational effort by nine orders of magnitude for three-dimensional flows, compared with direct numerical simulations using a uniform grid, and it will therefore enable affordable simulations of practical flows in industry.

Keywords: moving contact lines,dynamic wetting,thermocapillarity,macroscale computational model
The first cybersecurity management system providing evidence based metrics for cyber risk at the business asset level in real-time (STORM)

Framework: Horizon 2020 — Societal Challenges
Programme: Secure societies - protecting freedom and security of Europe and its citizens
Action type: SME instrument
Countries involved: Israel
Call: Engaging SMEs in security research and development (H2020-SMEINST-1-2016-2017, 2017)
Start date: 2017-07-01
End date: 2017-10-31
Duration (months): 4.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

InnoSec’s main product, STORM, is the only Cyber Risk Management application that provides evidence based metrics defining cyber risk at the business asset level in real-time, using a flexible risk modeling method, while improving the overall user experience. STORM allows companies to answer strategic questions such as “How much cyber risk do we have?”, “How well are our assets protected against a cyber-attack?”, “What work is in progress related to remediation of cyber risk?”, “How much cyber insurance do I need?” and “What should my customer pay for cyber insurance?” STORM is designed to solve the pains of seeing the cyber risk picture across all aspects of the enterprise including the CEO/board, CISO, Compliance Manager, Risk Manager and Cyber Security Remediators. STORM, is a complete risk management framework that breaks the bounds of the traditional siloed risk management solutions and connects the dots between strategic and operational cyber. STORM allows the organization to collaborate on cyber risk management activities through information sharing, advanced analysis tools, flexible workflows, operational management, smart dashboards, a dynamic reporting engine and interaction with the existing user workspace. STORM is a comprehensive solution for cyber risk management and decision support, and provides business intelligence that enables organizations to plan, measure, manage and monitor their cyber-security posture.

Keywords: cyber,cybersecurity,security
Elbee, a worldwide unique drive-from-wheelchair vehicle (Elbee)

Framework: Horizon 2020 — Societal Challenges
Programme: Smart, green and integrated transport
Action type: SME instrument
Countries involved: Czechia
Call: Small business innovation research for Transport and Smart Cities Mobility (H2020-SMEINST-1-2016-2017, 2016)
Start date: 2017-03-01
End date: 2017-08-31
Duration (months): 6.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

Elbee is a revolutionary vehicle that brings increased mobility to the life of the wheelchair users, for some of them not possible to gain in another way. Without it, they are often unproductive and dependent on help from others. The Elbee vehicle fills a significant market gap. The aim of this project is drawing up the documentation for implementating the arrangements for a massive entry to the global market. Specific objectives for the feasibility study are a summary of relevant requirements on product innovation, analysis of the feasibility of selected upgrade requirements, a proposal for the extension of the legal protection and verification of freedom to operate and business plan update. The project belongs to the TRL 7 definition (paying customers). For a more massive commercialisation it is necessary to innovate it in a way for it to meet the individual needs of the largest possible spectrum of wheelchair users. Worldwide challenges addressed are equal opportunities, employment, aging and integration of the minorities. The target group is persons unable to move to a car seat independently (e.g. those using an electric wheelchair). Specific needs of these persons are to drive without the need for assistance, from their own wheelchair, with diverse degrees of disability, etc. Selling points are functionality (drive-from-wheelchair vehicle, perpendicular parking, front exit, etc.), playfulness and design. Elbee targets a billion EUR market. According to WHO, 1% of the world population are wheelchair users (70 mn people). The investment into the special is very logical and offers a great return, both financial and social. Rapid global expansion is possible thanks to B2B sales partners. The total amount needed for mass expansion is estimated at EUR 4 mn. The company will draw this amount primarily from own resources. To accelerate the expansion it is possible to use Phase 2 of the SME Instrument and/or the investors.

Keywords: wheelchair,disabled mobility,elbee,vehicle
COMPUTATIONAL EVOLUTION OF ENZYME VARIANTS THROUGH CONFORMATIONAL NETWORKS (EnzVolNet)

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-2016, 2016)
Start date: 2017-05-01
End date: 2019-04-30
Duration (months): 24.0
Total project cost (EUR): 158121.6
EU funding (EUR): 158121.6
Participants: 1
Project status: CLOSED

Natural enzymes have evolved to perform their functions under complex selective pressures, being capable of accelerating reactions by several orders of magnitude. In particular, heteromeric enzyme complexes catalyze an enormous array of useful reactions that are often allosterically regulated by different protein partners. Unfortunately, the underlying physical principles of this regulation are still under debate, which makes the alteration of enzyme structure towards useful isolated subunits a tremendous challenge for modern chemical biology. Exploitation of isolated enzyme subunits, however, is advantageous for biosynthetic applications as it reduces the metabolic stress on the host cell and greatly simplifies efforts to engineer specific properties of the enzyme. Current approaches to alter natural enzyme complexes are based on the evaluation of thousands of variants, which make them economically unviable and the resulting catalytic efficiencies lag far behind their natural counterparts. The revolutionary nature of EnzVolNet relies on the application of conformational network models (e.g Markov State Models) to extract the essential functional protein dynamics and key conformational states, reducing the complexity of the enzyme design paradigm and completely reformulating previous computational design approaches. Initial mutations are extracted from costly random mutagenesis experiments and chemoinformatic tools are used to identify beneficial mutations leading to more proficient enzymes. This new strategy will be applied to develop stand-alone enzymes from heteromeric protein complexes, with advantageous biosynthetic properties and improve activity and substrate scope. Experimental evaluation of our computational predictions will finally elucidate the potential of the present approach for mimicking Nature’s rules of evolution.

Keywords: computational evolution of enzymes,markov state models,molecular dynamics,qmmm
Cognitive tomography of mental representations (COGTOM)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: The Human Mind and Its Complexity
Panel description: Cognitive science, psychology, linguistics, theoretical philosophy
Countries involved: Hungary
Call: ERC Consolidator Grant (ERC-2016-COG, 2016)
Start date: 2017-05-01
End date: 2023-04-30
Duration (months): 72.0
Total project cost (EUR): 1179462.0
EU funding (EUR): 1179462.0
Participants: 1
Project status: SIGNED

Internal models are fundamental to our understanding of how the mind constructs percepts, makes decisions, controls movements, and interacts with others. Yet, we lack principled quantitative methods to systematically estimate internal models from observable behaviour, and current approaches for discovering their mental representations remain heuristic and piecemeal. I propose to develop a set of novel 'doubly Bayesian' data analytical methods, using state-of-the-art Bayesian statistical and machine learning techniques to infer humans' internal models formalised as prior distributions in Bayesian models of cognition. This approach, cognitive tomography, takes a series of behavioural observations, each of which in itself may have very limited information content, and accumulates a detailed reconstruction of the internal model based on these observations. I also propose a set of stringent, quantifiable criteria which will be systematically applied at each step of the proposed work to rigorously assess the success of our approach. These methodological advances will allow us to track how the structured, task-general internal models that are so fundamental to humans' superior cognitive abilities, change over time as a result of decay, interference, and learning. We will apply cognitive tomography to a variety of experimental data sets, collected by our collaborators, in paradigms ranging from perceptual learning, through visual and motor structure learning, to social and concept learning. These analyses will allow us to conclusively and quantitatively test our central hypothesis that, rather than simply changing along a single 'memory strength' dimension, internal models typically change via complex and consistent patterns of transformations along multiple dimensions simultaneously. To facilitate the widespread use of our methods, we will release and support off-the-shelf usable implementations of our algorithms together with synthetic and real test data sets.

Keywords: internal model,bayesian modelling,bayesian inference,computational cognitive science,observational learning,motor learning,statistical learning,learning curve,forgetting
Graphene-Manganite Nanostructures for Novel Pulsed Magnetic Field Sensors (GRAMAS)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Career Restart panel
Action type: Marie Skłodowska-Curie actions
Panel: Chemistry
Countries involved: Lithuania
Call: Individual Fellowships (H2020-MSCA-IF-2016, 2016)
Start date: 2017-04-01
End date: 2019-03-31
Duration (months): 24.0
Total project cost (EUR): 142779.6
EU funding (EUR): 142779.6
Participants: 1
Project status: CLOSED

A two-way innovative approach of combining Graphene and novel La1-xSrxMn1-yCoyO3 manganite is proposed in this GRAMAS project in order to manufacture highly sensitive sensors, measuring pulsed magnetic field [0.5 – 60 T] independently on its direction (so-called B-scalar sensors). Firstly, the Co3+ cations will be introduced into the Mn3+ sites in order to achieve a novel, nanostructured La1-xSrxMn1-yCoyO3 layers deposited on various substrates by MOCVD technique. Secondly, the single/few-layers of Graphene will be transferred on the top of Co-doped manganite layers, since Graphene has a superior mobility of charge carriers which also increases the Magnetoresistance. In addition, Graphene layer will allow indication of magnetic field direction respectively to manganite B-scalar sensors‘s plane. Moreover, the combination of manganite/Graphene is expected to open new potentials for the discovery of superior magnetotransport properties even at room temperatures and increased sensitivity to magnetic field. A full spectrum of investigations, including determination of composition and the crystallinity of the films, electrical and magnetotransport properties dependence on the chosen substrate, growth conditions, number of Graphene layers will be performed in order to achieve the highest magnetoresistance at room temperature for the development of highly sensitive B-scalar magnetic field sensors.

Keywords: mocvd growth,nanostructured films,doped lanthanum manganites,graphene,magnetoresistance,high magnetic fields,magnetic field sensors
Deep learning for mammography: Improving accuracy and productivity in breast cancer diagnosis. (MAMMO1)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: United Kingdom
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2018)
Start date: 2019-01-01
End date: 2019-04-30
Duration (months): 4.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

Breast cancer is one of the main causes of death among women worldwide. Early diagnosis by mammography scanning is the best way to prevent mortality, but it requires the intervention of a highly trained workforce (radiologists). While the demand for radiologists is on the rise, the supply is quickly diminishing worldwide. This leads to long waiting lists and delays in getting a diagnosis, negatively affecting quality of services and ultimately survival rates. There is a strong need for tools that help radiologists make accurate decisions on mammography images in less time. CAD-based systems were developed to address this need; however, they have very low specificity, which leads to a high number of false positives, unnecessarily increasing the recall rates, and raising doubts about their usefulness. Mammo1 will be a game-changer in the area of breast cancer diagnosis by applying ground-breaking machine learning techniques, which are able to outperform all the currently marketed CAD-based solutions and even single radiologists.

Keywords: breast cancer,mammography,deep learning,convolutional neural networks
The Migration Challenge: Labour Markets, Policy Reforms, and Social Cohesion (MCLPS)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Individuals, Markets and Organisations
Panel description: Economics, finance and management
Countries involved: United Kingdom
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): 2099223.0
EU funding (EUR): 2099223.0
Participants: 1
Project status: SIGNED

International migration is among the most important issues that have shaped the policy debates and political landscape in Europe in recent decades. Both the intra-European migration of workers and the influx of refugee migrants from outside Europe pose enormous economic and political challenges to Europe in the coming decades. This proposed research will investigate three key areas in which migration can have fundamental consequences for both economies and societies: (i) The Impact of Migration on Workers, Firms, and Productivity; (ii) Migration and the Rise of Populism; and (iii) Immigration, Integration and the Policies of the Welfare State. The specific questions to be addressed include: How does the policy of free mobility affect workers and firms in sending and receiving countries, through channels other than the standard demand-supply mechanism? How does immigration affect citizens’ voting behaviour? In particular, what are the roles of past and present economic circumstances and the media in driving the relationship? What are the intended and unintended, and immediate and long term effects of policy reforms on residency rights of and welfare transfers to refugee migrants? The research will combine innovative theoretical perspectives with state-of-the-art empirical analyses, exploiting linked employer-employee datasets and population registers, as well as previously unexplored data sources on voting and electoral outcomes to uncover important causal relations. All the proposed research questions, while focused on immigration, also speak to different core issues on the frontier of economics research, including inequality, rent sharing between firms and workers, productivity, political economy, and welfare reforms. The results of this investigation will thus not only provide relevant insights for contemporary immigration policies in Europe but deepen our core understanding of the economy that should reach well beyond it.

Keywords: migration,geographic labor mobility,immigrant workers,refugees,wages,inequality,government policy,provision and effects of welfare programs,populism,elections,voting behavior