Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Layered functional materials - beyond 'graphene' (BEGMAT)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting 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: Germany,Czechia
Call: ERC Starting Grant (ERC-2015-STG, 2015)
Start date: 2016-08-01
End date: 2021-07-31
Duration (months): 60.0
Total project cost (EUR): 1362537.5
EU funding (EUR): 1198977.5
Participants: 2
Project status: CLOSED

There is an apparent lack of non-metallic 2D-matrials for the construction of electronic devices, as only five materials of the “graphene family” are known: graphene, hBN, BCN, fluorographene, and graphene oxide – none of them with a narrow bandgap close to commercially used silicon. This ERC-StG proposal, BEGMAT, outlines a strategy for design, synthesis, and application of layered, functional materials that will go beyond this exclusive club. These materials “beyond graphene” (BEG) will have to meet – like graphene – the following criteria: (1) The BEG-materials will feature a transfer of crystalline order from the molecular (pm-range) to the macroscopic level (cm-range), (2) individual, free-standing layers of BEG-materials can be addressed by mechanical or chemical exfoliation, and (3) assemblies of different BEG-materials will be stacked as van der Waals heterostructures with unique properties. In contrast to the existing “graphene family”, (4) BEG-materials will be constructed in a controlled way by covalent organic chemistry in a bottom-up approach from abundant precursors free of metals and critical raw materials (CRMs). Moreover – and unlike – many covalent organic frameworks (COFs), (5) BEG-materials will be fully aromatic, donor-acceptor systems to ensure that electronic properties can be addressed on macroscopic scale. The potential to make 2D materials “beyond graphene” is a great challenge to chemical bond formation and material design. In 2014 the applicant has demonstrated the feasibility of the concept to expand the “graphene family” with triazine-based graphitic carbon, a compound highlighted as an “emerging competitor for the miracle material” graphene. Now, the PI has the opportunity to build a full-scale research program on layered functional materials that offers unique insights into controlled, covalent linking-chemistry, and that addresses practicalities in device manufacture, and structure-properties relationships.
Testing Cyber-Physical Systems under Uncertainty: Systematic, Extensible, and Configurable Model-based and Search-based Testing Methodologies (U-Test)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Theme: A new generation of components and systems: Engineering of advanced embedded and energy and resource efficient components and systems
Action type: Research and Innovation actions
Countries involved: Austria,Spain,Germany,Sweden,Sweden,Norway,Norway,France
Call: Smart Cyber-Physical Systems (H2020-ICT-2014-1, 2014)
Start date: 2015-01-01
End date: 2017-12-31
Duration (months): 36.0
Total project cost (EUR): 3713233.75
EU funding (EUR): 3713233.75
Participants: 10
Project status: CLOSED

Uncertainty is intrinsic in Cyber-Physical Systems (CPSs) due to novel interactions of embedded systems, networking equipment, cloud infrastructures, and humans. CPSs have become predominant in critical domains and necessitate the implementation of proper mechanisms to deal with uncertainty during their operation at an acceptable cost avoiding unwarranted threats to its users and environment. One way to guarantee the correct implementation of such mechanisms is via automated and systematic Model-Based Testing (MBT)—a way of improving dependability. U-Test will improve the dependability of CPSs by defining extensible MBT frameworks supporting holistic MBT of CPSs under uncertainty in a cost-effective manner. More specifically our objectives are: 1) Provide a comprehensive and extensible taxonomy of uncertainties classifying uncertainties, their properties, and relationships; 2) An Uncertainty Modelling Framework (UMF) to support modelling uncertainties at various levels relying on exiting modelling/testing standards; 3) Defining an intelligent way to evolve uncertainty models developed using UMF towards realistic unknown uncertainty models using search algorithms (e.g., Genetic Algorithms); 4) Generating cost-effective test cases from uncertainty and evolved models. U-Test consortium encompasses domain experts from various facets of CPSs, i.e., software, embedded systems, distributed systems, and cloud infrastructure. We have chosen two case studies from diverse domains including Handling Systems and Geo Sports to assess the cost-effectiveness of U-Test. The solutions will be integrated into two key commercial tools available in the market: ModelBus/Fokus!MBT and CertifyIt. Moreover, the solutions will be deployed into the actual practise in addition to standardization to achieve a wider impact within Logistics, Geo Sports, and Healthcare domains and further facilitate interoperability among tools and technologies.

Keywords: cyber physical systems,model based testing,search algorithms,uncertainty,dependability,test strategies,test data,testing standards,cloud infrastructure,taxonomy
Improving loan quality and acceptance rates by predicting credit behavior through social media data. (BigDataScore)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Action type: SME instrument
Countries involved: Estonia
Call: Open Disruptive Innovation Scheme (implemented through the SME instrument) (H2020-SMEINST-1-2014, 2014)
Start date: 2015-03-01
End date: 2015-06-30
Duration (months): 4.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 2
Project status: CLOSED

The problem: Credit Losses on Banks’ Loan Portfolios - Youngsters and 2nd generation immigrants still have difficulty in obtaining credit. The solution: Our credit scoring model called Big Data Score assesses the credit quality of people and accurately predicts their payment behaviour based on data from social media (Facebook) and internet browsing behaviour. Objectives of the overall innovation project: 1) Bring the present Technology Readiness from level 7 to 9. 2) Provide the system with complete access to real life data and Open Data made available from governments. 3) Development of a marketing and sales strategy based on two key principle: vertical approach and distribution approach. Value Proposition: To help lenders to save money on credit losses and to make more money on increased acceptance rate. Business Model: Business follows a simple and easily scalable model where lender pays for each score: 0.99 EUR per Facebook score and 0.20 EUR per browser score. Users/Clients: Our target client is anyone who is taking a short to medium term (1-36 months) credit risk. Competition: Traditional credit bureaus and innovative credit score (Kreditech, Leendo, ZestFinance). Revenue Streams: 36M€ revenues at the 3rd year after commercialization. Team: Big Data Scoring AS, Aasa Global AS. Required funding: 1,5M€

Keywords: banks,financial institutions,loan quality,innovative model score
Dendrites and memory: role of dendritic spikes in information coding by hippocampal CA3 pyramidal neurons (DeCode)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Neuroscience and Disorders of the Nervous System
Panel description: Nervous system development, homeostasis and ageing, nervous system function and dysfunction, systems neuroscience and modelling, biological basis of cognitive processes and of behaviour, neurological and mental disorders
Countries involved: Hungary
Call: ERC Consolidator Grant (ERC-2017-COG, 2017)
Start date: 2018-06-01
End date: 2024-11-30
Duration (months): 78.0
Total project cost (EUR): 1990314.0
EU funding (EUR): 1990314.0
Participants: 1
Project status: SIGNED

The hippocampus is essential for building episodic memories. Coding of locations, contexts or events in the hippocampus is based on the correlated activity of neuronal ensembles; however, the mechanisms promoting the recruitment of individual neurons into information-coding ensembles are poorly understood. In particular, the recurrent synaptic network of pyramidal cells (PCs) in the hippocampal CA3 area, receiving external inputs from the entorhinal cortex and the dentate gyrus, is thought to be essential for associative memory. Current models of the associative functions of CA3 are mainly based on plasticity of these synaptic connections. Recent work by us and others however suggests that active, voltage-dependent properties of CA3PC dendrites may also promote ensemble functions. Dendritic voltage-dependent ion channels allow nonlinear amplification of spatiotemporally correlated synaptic inputs (such as those produced by ensemble activity) and can even generate local dendritic spikes, which may elicit specific action potential patterns and induce synaptic plasticity. Furthermore, dendritic processing may be modulated by activity-dependent regulation of dendritic ion channels. However, still little is known about the active properties of CA3PC dendrites and their functions during spatial coding or memory tasks. The general aim of my research program is to understand the cellular mechanisms that underlie the formation of hippocampal memory-coding neuronal ensembles. Specifically, we will test the hypothesis that active input integration by dendrites of individual CA3PCs plays an important role in their recruitment into specific context-coding ensembles. By combining in vitro (patch-clamp electrophysiology and two-photon (2P) microscopy in slices) and in vivo (2P imaging and activity-dependent labelling in behaving rodents) approaches, we will provide an in-depth understanding of the dendritic components contributing to the generation of the CA3 ensemble code.
Automated Model Inference from Neural Dynamics for a Mechanistic Understanding of Cognition (AutoMIND)

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: Germany
Call: Individual Fellowships (H2020-MSCA-IF-2020, 2020)
Start date: 2021-05-01
End date: 2023-04-30
Duration (months): 24.0
Total project cost (EUR): 162806.4
EU funding (EUR): 162806.4
Participants: 1
Project status: SIGNED

Human cognition depends on complex coordinated dynamics of neural populations, which is shaped by a rich heterogeneity in cellular properties and network connectivity patterns of neural circuits. While cognitive neuroscience leverages macroscopic brain signals to relate neural activity to behavioral states, we currently cannot dissect them for their physiological contributions, hindering mechanistic interpretations of experimental data. One way to systematically study how physiological parameters shape neural dynamics is through mechanistic modeling of spiking neural networks. However, current modeling approaches are not quantitatively constrained by observed electrophysiological data, and often require painstaking and ad-hoc parameter-tuning by hand. Efficient discovery of mechanistic models that are consistent with experimental data would dramatically accelerate our understanding of how cellular and network properties impact cognition, and why it breaks down in pathological states, representing a radical departure from how neural data is analyzed in cognitive neuroscience. To this end, I propose to develop a machine learning-assisted model inference tool—Automated Model Inference from Neural Dynamics (AutoMIND)—that can identify parameters of candidate spiking neural network models that could capture arbitrary target neural dynamics from human brain recordings. AutoMIND extends on recent advances in simulation-based inference techniques, incorporating simultaneous parameter manifold-learning and gradient-based simulations. AutoMIND has broad utility for tackling neuroscientific questions by enabling expedited in-silico experiments. Here, I apply it to multiscale neural data to study how cellular and network properties shape: 1) the emergence of synchronous network oscillations during early neurodevelopment, and 2) the difference in neural dynamics and computation between sensory and association cortices—two questions of fundamental importance to neuroscience.

Keywords: simulation based inference,likelihood free inference,mechanistic modeling,spiking neural networks
Real operation pem fuel cells HEALTH-state monitoring and diagnosis based on dc-dc COnverter embeddeD Eis (HEALTH-CODE)

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: Italy,Germany,Italy,Italy,France,France,France,Denmark
Call: Stationary fuel cell system diagnostics: development of online monitoring and diagnostics systems for reliable and durable fuel cell system operation (H2020-JTI-FCH-2014-1, 2014)
Start date: 2015-09-01
End date: 2018-12-31
Duration (months): 40.0
Total project cost (EUR): 2358736.25
EU funding (EUR): 2358736.25
Participants: 12
Project status: CLOSED

HEALTH-CODE aims at implementing an advanced monitoring and diagnostic tool for μ-CHP and backup PEM fuel cell systems equipped with different stacks. Such a tool is able to determine the FC current status (condition monitoring) to support stack failures detection and to infer on the residual useful lifetime. Five failure modes will be detected: i) change in fuel composition; ii) air starvation; iii) fuel starvation; iv) sulphur poisoning; v) flooding and de-hydration. The main project objectives are: i) the enhancement of electrochemical impedance spectroscopy (EIS) based diagnosis; ii) the development of a monitoring and diagnostic tool for state-of-health assessment, fault detection and isolation as well as degradation level analysis for lifetime extrapolation; iii) the reduction of experimental campaign time and costs. Moreover, the improvement of power electronics for FC is also considered. These targets will be achieved through the implementation of several methodologies and techniques, well suited for industrial application. Several algorithms will be developed relying on on-board EIS measurements of the fuel cell system impedance. Moreover, low-cost diagnostic concepts are also proposed for a straightforward implementation on FCS controllers. The project exploits the outcomes of the previous FCH 1 JU funded project D-CODE, during which a proof of-concept validated in laboratory (TRL3-4) was developed. HEALTH-CODE will increase the TRL up to level 5. The exploitation of the project outcomes will lead to low-cost and reliable monitoring and diagnostic approaches and related applications (e.g. power electronics). These results will have an impact on stationary FCS with a direct increase in electrical efficiency, availability and durability, leading to a reduction in maintenance and warranty costs, thus increasing the customers’ satisfaction. Therefore, HEALTH-CODE contributes to the enhancement of FC competitiveness towards a wider market deployment.

Keywords: fuel cells,backup power,monitoring,diagnosis,lifetime,power electronics
Photonic integrated quantum transceivers (PINQS)

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,Germany
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): 1989812.5
EU funding (EUR): 122500.0
Participants: 2
Project status: SIGNED

Quantum processors are envisioned to conquer ultimate challenges in information processing and to enable simulations of complex physical processes that are intractable with classical computers. Among the various experimental approaches to implement such devices, scalable technologies are particularly promising because they allow for the realization of large numbers of quantum components in circuit form. For upscaling towards functional applications distributed systems will be needed to overcome stringent limitations in quantum control, provided that high-bandwidth quantum links can be established between the individual nodes. For this purpose the use of single photons is especially attractive due to compatibility with existing fibre-optical infrastructure. However, their use in replicable, integrated optical circuits remains largely unexplored for non-classical applications. In this project nanophotonic circuits, heterogeneously integrated with superconducting nanostructures and carbon nanotubes, will be used to realize scalable quantum photonic chips that overcome major barriers in linear quantum optics and quantum communication. By relying on electro-optomechanical and electro-optical interactions, reconfigurable single photon transceivers will be devised that can act as broadband and high bandwidth nodes in future quantum optical networks. A hybrid integration approach will allow for the realization of fully functional quantum photonic modules which are interconnected with optical fiber links. By implementing quantum wavelength division multiplexing, the communication rates between individual transceiver nodes will be boosted by orders of magnitude, thus allowing for high-speed and remote quantum information processing and quantum simulation. Further exploiting recent advances in three-dimensional distributed nanophotonics will lead to a paradigm shift in nanoscale quantum optics, providing a key step towards optical quantum computing and the quantum internet.

Keywords: nanophotonics,optomechanics,carbon photonics
Reactivity and Assembly of Multifunctional, Stimuli-responsive Encapsulation Structures (RAMSES)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator 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: Germany
Call: ERC Consolidator Grant (ERC-2015-CoG, 2015)
Start date: 2016-12-01
End date: 2022-11-30
Duration (months): 72.0
Total project cost (EUR): 1982500.0
EU funding (EUR): 1982500.0
Participants: 1
Project status: CLOSED

In biochemical systems, combinations of specialized molecular entities are precisely arranged to give highly complex architectures. Sophisticated functionality, such as the selective chemical transformation of substrates in enzymes, emerges from the interplay of the individual components that are often grouped around a nanoscopic cavity. Control mechanisms based on the cooperative binding of signal substances regulate the enzyme’s action, and complicated feedback loops may apply. Since the advent of supramolecular chemistry, scientists construct artificial systems with ever increasing complexity and functionality that promise to serve as the basis for future developments in bottom-up nanotechnology with applications in medicine (drug delivery), diagnostics, catalysis, material science and molecular photonics/electronics. Self-assembly of functional entities with pre-programmed connectivities has produced an impressive line-up of nanoscopic architectures such as coordination cages that recognize and transform molecular substrates. Most of these systems are based on one sort of ligand, joined by one kind of metal ion. My group has reported a number of cages, each equipped with a unique, single function such as chirality, redox-activity, light-switching, allosteric regulation or endohedral binding sites. While all these mono-functionalized cages contribute to the progress of supramolecular architecture, nature demonstrates that the key to the most sophisticated systems lies in multi-functionalized structures. As breakthrough strategies for achieving this level of complexity with artificial systems we propose: 1) Heteroleptic coordination of ligands by a [Pd2Ligand4]-platform-specific way of steric fine-tuning 2) Biopolymer-inspired folding of a modular chain of covalently joined building blocks Combined with our recent achievements in host-guest switching, we aim at adjustable receptors, controllable molecular reaction chambers and multifunctional photo/redox systems

Keywords: self assembly,coordination cages,host guest chemistry,systems chemistry,multifunctionality
Community-based cyclist navigation solution to increase safety of utility bikers (SHOKA)

Framework: Horizon 2020 — Societal Challenges
Programme: Smart, green and integrated transport
Action type: SME instrument
Countries involved: Hungary
Call: Small business innovation research for Transport and Smart Cities Mobility (H2020-SMEINST-1-2016-2017, 2017)
Start date: 2017-07-01
End date: 2017-09-30
Duration (months): 3.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

In the European Union some 8% of citizens are using bicycle the most often among transport modes, and the number of cyclist commuters is around 40 million. Facing uncomfortable traffic conditions is however a real obstacle for many people to use their bike even for minor journeys. In Shoka project we will present the world’s first interactive, community-based navigation solution specifically designed for the specific needs of cyclists. The project will directly contribute to increasing the safety and efficiency of cycling in urban areas by recommending the community data based safest route for the users. Our disruptive solution offers a pragmatic and focused approach: robust, practical and simple hardware (ShokaBell) on the handlebar and the Shoka navigation app running on the smartphone of the user. Shoka Bell acts as a display to show easily visible and understandable signals and also as a data collection platform: in-built gyroscope, accelerometer, noise detector and an easy-to-use joystick for user feedback. Bluetooth communication sends the data to the smartphone which is in real-time access with the cloud, where community-based data is stored and where route planning is created. Our Shoka Bell and App are in beta test stage and under preparation for mass manufacturing and sales (TRL6). Shoka will be sold in a hardmium business model: the customer purchases the Shoka Bell hardware but the value comes from the hardware-service combination. We will generate sales revenue through the initial purchase of the hardware, while the app is free for the customers. We sold more than 1500 units of ShokaBell during our Kickstarter campaign, which is a clear proof for willingness to pay. Our total available market is 40 million commuters only in Europe and we estimate our serviceable obtainable market as 4 million people in the EU. Our financial plans are calculating with 10.3 million € in sales revenues between 2017 and 2020.

Keywords: urban cycling