Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Solid Catalysts for activation of aromatic C-H bonds (H-CCAT)

Framework: Horizon 2020 — Industrial Leadership
Programme: Nanotechnologies, Advanced Materials, Advanced Manufacturing and Processing, and Biotechnology
Theme: Advanced materials
Action type: Research and Innovation actions
Countries involved: Germany,Italy,Germany,Norway,Belgium,Belgium,France,France,France,United Kingdom
Call: Novel hybrid materials for heterogeneous catalysis (H2020-NMBP-2016-two-stage, 2016)
Start date: 2017-01-01
End date: 2021-06-30
Duration (months): 54.0
Total project cost (EUR): 7588312.5
EU funding (EUR): 7588312.5
Participants: 11
Project status: CLOSED

Objectives The H-CCAT project designs, upscales and shapes hybrid catalysts for the C-H functionalization of aromatic compounds. These solid catalysts will possess better recoverability, higher turnover numbers and better selectivity than current homogeneous catalysts for these reactions. The solid catalysts are applied at demonstration scale in the step-economical production of arylated or alkenylated aromatics, yielding motifs of active pharmaceutical ingredients. Methodology We will design heterogeneous hybrid catalysts featuring deactivation-resistant active sites, based on N-heterocyclic carbenes (NHCs) or diimine ligands and active metal ions. Via efficient, one-step protocols based on self-assembly, these sites will be embedded in robust porous hybrid materials like hybrid silica or metal-organic frameworks. Deactivation or metal aggregation will be prevented by site isolation or by efficient metal reoxidation (for the oxidative alkenylations). Metal leaching is precluded by using strong bonds between metals and embedded ligands like NHCs. Flow protocols will be designed to maximize the turnover numbers. Catalyst synthesis will be scaled up to kg scale, using efficient one-step protocols, minimizing use of solvents or waste formation. Soft shaping methods, e.g. spray drying, will preserve porosity and activity of the hybrid solids. A demonstration is conducted at minipilot scale at the J&J site (Belgium), allowing LCA analysis, techno-economic assessment and elaboration of the business plan. Relevance to work program The catalysts feature new, deactivation resistant active sites; their TOF/TON is maximized by an appropriate porous structure which even can be swelling. Catalysts are produced using innovative one-step protocols to form porous hybrid catalysts as powders or even immediately as shaped objects. The molecules targeted have strong biological and pharmaceutical relevance; they target diseases like influenza, cancer or HIV (case study: Rilpivirine).

Keywords: c h activation,porous solids,hybrid silica,metal organic frameworks,activity,positional selectivity,shaping,upscaling,silylated ligands,flow synthesis,rilpivirine,pharmaceutics
An innovative peptide system for novel drugs targeting microbial Repeats-In-Toxin adhesins (PEPTOX)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Proof of concept grants
Action type: ERC actions
Panel: Synthetic Chemistry and Materials
Panel description: New materials and new synthetic approaches, structure-properties relations, solid state chemistry, molecular architecture, organic chemistry
Countries involved: Netherlands
Call: ERC Proof of Concept Grant (ERC-2019-PoC, 2019)
Start date: 2020-09-01
End date: 2022-11-30
Duration (months): 27.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: CLOSED

Despite progress in infection treatment in the last decades, the prevalence and mortality of infections still poses a heavy burden over humankind. The development of antimicrobial resistance and the rapid mutagenesis of bacteria that leads to inefficiency of vaccines, play an important role in maintaining infections high level of threat. Even diseases that have plagued us for centuries, such as cholera, still challenge medical professionals and cause a significant social cost. New therapeutic approaches that promise a higher success rate are a pressing need. We recently discovered a high similarity between an ice-binding protein and the RTX adhesin (FrhA) used by the bacteria of cholera to attach to and infect the human intestine. Moreover, we discovered compounds that bind to FrhA and successfully block it, suggesting these could help prevent cholera’s infection. If these can also detach already adhered bacteria, cholera could be effectively removed and cured. In this ERC PoC PEPTOX project, we aim to optimise the synthesis and characterization of the newly discovered compounds and demonstrate blockage of cholera adhesion in an in vitro cellular model.

Keywords: cholera,peptides,therapeutics,bacterial infection,bacterial adhesion,rtx adhesin,frha,gram negative bacteria
An End to end Interoperability Framework For MaritimE Situational Awareness at StrategiC and TacTical OpeRations (EFFECTOR)

Framework: Horizon 2020 — Societal Challenges
Programme: Secure societies - protecting freedom and security of Europe and its citizens
Theme: Strengthen security through border management
Action type: Innovation actions
Countries involved: Italy,Bulgaria,Greece,Greece,Greece,France,France,France,Montenegro,Portugal
Call: Demonstration of applied solutions to enhance border and external security (H2020-SU-SEC-2019, 2019)
Start date: 2020-10-01
End date: 2022-09-30
Duration (months): 24.0
Total project cost (EUR): 5882380.0
EU funding (EUR): 4999529.5
Participants: 20
Project status: CLOSED

EFFECTOR aims to enhance maritime surveillance, improve decisions support, and foster collaboration of maritime stakeholders by implementing an Interoperability Framework and associated Data Fusion and Analytics services for Maritime Surveillance and Border Security that will allow faster detection of new events, better informed decision making, achievement of a joint understanding and undertaking of a situation across borders, allowing seamless cooperation between operating authorities and on-site intervention forces ensuring that all existing privacy and data protection rules are fully respected. Specifically, EFFECTOR will unlock the full capabilities of maritime surveillance systems and data sharing at tactical and strategic level by introducing applied solutions for enhanced border and external security, including the implementation of a multilayered data lake platform for end-to-end interoperability and data exploitation, the exchange of enhanced situational awareness pictures at different level with CISE and EUROSUR, the adoption of interoperability standards for exploiting data sources and systems currently underutilized in maritime environment and the demonstration of new concepts and tools for knowledge extraction, semantic representation, data fusion, analytics, and federated querying that can scale from local to regional and up to national and transnational level. The EFFECTOR solution will be tested, validated and demonstrated in real operational scenarios together with maritime authorities, End Users and practitioners in France, Portugal and Greece. The project will leverage on the developments, results and experience from current and previous research projects (EUCISE2020, MARISA, RANGER, PERSEUS, BLUEMASSMED), from National Procurement projects of CISE Nodes and Adaptors and on the CISE infrastructure of the End Users.

Keywords: interoperability,information systems,maritime surveillance systems,assets,cise,eurosur,situational awareness,national,regional,local coordination centers
Data Learning on Manifolds and Future Challenges (DEDALE)

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: Germany,Greece,France,France,United Kingdom
Call: FET-Open research projects (H2020-FETOPEN-2014-2015-RIA, 2014)
Start date: 2015-10-01
End date: 2018-09-30
Duration (months): 36.0
Total project cost (EUR): 2702397.5
EU funding (EUR): 2702397.5
Participants: 5
Project status: CLOSED

Future data processing challenges in science will enter the "Big Data" era, involving massive, as well as complex and heterogeneous data. Extracting, with high precision, every bit of information from scientific data requires overcoming fundamental statistical challenges, which mandate the design of dedicated methods that must be both effective enough to capture the intricacy of real-world datasets and robust to the high complexity of instrumental measurements. Moreover, future datasets, such as those provided by the space mission Euclid, will involve at least gigascale data, which will make mandatory the development of new, physically relevant, data models and the implementation of effective and computationally efficient processing tools. The recent emergence of novel data analysis methods in machine learning should foster a new modeling framework, allowing for a better preservation of the intrinsic physical properties of real data that generally live on intricate spaces, such as signal manifolds. Furthermore, advances in operations research and optimization theory pave the way for effective solutions to overcome the large-scale data processing bottlenecks. In this context, the objective of the DEDALE project is threefold: i) introduce new models and methods to analyze and restore complex, multivariate, manifold-based signals; ii) exploit the current knowledge in optimization and operations research to build efficient numerical data processing algorithms in the large-scale settings; and iii) show the reliability of the proposed data modeling and analysis technologies to tackle Scientific Big Data challenges in two different applications: one in cosmology, to map the dark matter mass map of the universe, and one in remote sensing to increase the capabilities of automatic airborne imaging analysis systems.

Keywords: wavelets,dictionary learning,manifold,weak lensing,remote sensing
Advanced spatio-temporal causal inference for climate research (CausalEarth)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting 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: Germany,Germany
Call: ERC STARTING GRANTS (ERC-2020-STG, 2020)
Start date: 2021-02-01
End date: 2026-01-31
Duration (months): 60.0
Total project cost (EUR): 1499631.0
EU funding (EUR): 1112593.61
Participants: 2
Project status: SIGNED

CausalEarth is an interdisciplinary project, aiming to improve our understanding of the interdependencies between major drivers (modes) of climate variability by developing novel statistical causal inference methods for both observations and model data. Disentangling the interdependencies of the major modes, such as El Nino Southern Oscillation and the North Atlantic Oscillation, is key to understand regional climate, and essential for process-based climate model evaluation. The modes' interdependencies are characterized by common drivers, indirect effects, nonlinearities, regime-dependence, and heterogeneous spatio-temporal causal relations. Currently, observational analyses are mostly based on the correlation of scalar (one-dimensional) time series derived from regional averaging or principal component analysis, restricted to supposed causal regimes, e.g., the winter season or phases of multi-decadal climate indices, where dependencies are expected to be stationary. Such scalar correlation approaches fall short in capturing the modes' complex regime-dependent spatio-temporal causal interdependencies. CausalEarth will develop innovative methods to move (1) from representing complex phenomena as scalar indices to learning spatio-temporal features, (2) from supposing causal regimes to learning them from data, and (3) from correlation to causal dependencies. To this end, CausalEarth will combine recent developments in machine learning with causal inference algorithms. These methods will be used to infer the causal interdependencies and drivers of major climate modes from observations and to construct the next generation of causal metrics for climate model evaluation. CausalEarth will push the limits of what can be learned from observational data about causal relations and drive model development towards breakthroughs in projecting our future climate.

Keywords: causal inference,machine learning,modes of variability,climate model evaluation,gaussian processes,kernel methods
The normalisation of natural philosophy: how teaching practices shaped the evolution of early modern science (NaturalPhilosophy)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Cultures and cultural production
Panel description: Literary studies, cultural studies, study of the arts, philosophy
Countries involved: Netherlands
Call: ERC Starting Grant (ERC-2018-STG, 2018)
Start date: 2019-01-01
End date: 2023-12-31
Duration (months): 60.0
Total project cost (EUR): 1480611.0
EU funding (EUR): 1480611.0
Participants: 1
Project status: SIGNED

Early modern natural philosophy underwent dramatic transformations that completely reshaped its conceptual framework and set of practices. The main contention of my ERC project is that teaching practices had a decisive and ‘normalising’ impact on the progressive dissemination, adaptation and selection of rival conceptions of natural philosophy. Normalisation occurs when historical actors collectively present certain tenets as crucial for the study of a discipline, and thus prescribe them as a necessary subject for teaching and learning. The overall aim of this ERC project is to determine and explain how the process of normalisation embedded in teaching practices shaped the evolution of early modern natural philosophy. To study normalisation, it is necessary to operate a systematic comparative investigation of hundreds of works through which natural philosophy was taught, learned and reshaped, both within and outside universities. The size of this corpus defies the traditional method of close reading used by historians of philosophy and science. I will meet this challenge by organically integrating close reading with digital ‘distant reading’. I will digitally transcribe a corpus of approximately 500 early modern works on natural philosophy, published in Britain, France and the Dutch Republic. Using digital tools to investigate how the networks of authors and concepts of natural philosophy co-evolved over time will allow me to identify textual excerpts that are representative of historical trends. By analysing these excerpts with close reading and assessing them against the digital results, I will determine and explain how normalisation shaped the evolution of natural philosophy. This project will boost the integration of digital approaches in the history of philosophy and science by producing a newly digitised corpus, tools customized for analysing early modern texts, and methodological reflections on their implementation.

Keywords: natural philosophy,normalisation,distant reading,history of early modern philosophy and science,history of universities,digital humanities
Detection of defective textile production (SMARTEX)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Portugal
Call: SME instrument (H2020-EIC-SMEInst-2018-2020-3, 2019)
Start date: 2020-02-01
End date: 2021-09-30
Duration (months): 20.0
Total project cost (EUR): 1464375.0
EU funding (EUR): 1025062.5
Participants: 1
Project status: CLOSED

SMARTEX is a company committed to reducing the Textile Industry defective production (from 5%) to 0%. Our dedicated and highly skilled team has made this possible by developing unique devices, which combine hardware and software built in-house to achieve such a goal. SMARTEX is able to detect defects during the production stage, offering an online software as a service (SaaS) based on Computer Vision, Machine Learning and Artificial Intelligence with the clear mission of becoming the main solution for automation of the inspection processes in the Knitting Textile Industry. Currently, SMARTEX devices are in a prototype stage (TRL 6), since real short-time environment tests have been successfully performed and the market-fit is also validated by multiple LOIs and prices agreed with potential clients, including Decathlon group, PVH (Calvin Klein, Tommy Hilfiger, etc.), Kering (Gucci, YSL), in different continents. SMARTEX’s disruptive hardware & software technology has been recognized with several grants and awards and has recently participated participate in the biggest hardware startup acceleration programme HAX (Shenzhen, China), backed by Smartex’s first investor SOSV (Sean O’Sullivan Ventures).

Keywords: textile industry,textile manufacturing
Faster and cheaper robots for object recognition in Waste sorting through AI and external robot control (WEXOBOT)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Sweden
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2019)
Start date: 2019-12-01
End date: 2020-03-31
Duration (months): 4.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

The European waste management is not efficient enough to keep up with the targets of the Waste Directive and the waste sorting is not as profitable as it could be with robotizing and object recognition. We introduce the solution to these challenges with the Waste External Robot Control WEXOBOT, a robotic waste sorting system which has the potential to revolutionize waste sorting by replacing low-performing hazardous manual jobs with highly efficient and fast autonomous robotic pickers. The major innovation is a unique self-learning system, which gathers data of its environment, makes smart decisions and moves a robot arm in an unpredictable environment faster than any other system on the market. We work with general purpose industrial robots and turn such mass-produced robots into special purpose robots. Byte Motion aims to support as many robots and brands as possible making them faster and easier to reprogram. The technical solution combines the latest VR/AR solutions for human-like robotic movements with existing robotic technologies to create a new standard for telerobotic and telepresence. As of January 2019, we have developed Byte Motion’s beta version of AI for object recognition which now can recognize glass bottles, PET, Tetra Pak, batteries, and hopefully, it expands to other materials and shapes. The commercialization of WEXOBOT will bring benefits to European society at large as well as to waste managing companies. There will be sustainable effects in economic, ecologic and social terms. For Byte Motion the innovation project will lead to a generation of €6.9 Million in turnover and creating 36 new job positions in a four-year perspective after market launch as well as creating IP rights from the outcome of the Project. The demand for the innovation and support of the proposed Project is presented through the 8 letters of support. The long-term goal of this project is to create a successful market introduction and growth path for Byte Motion’s innovation

Keywords: object recognition,material recognition,waste sorting robot,external robot control,artificial intelligence
Polarization tracking in situ (POLARIS)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Proof of concept grants
Action type: ERC actions
Panel: Condensed matter physics
Panel description: Structure, electronic properties, fluids, nanosciences, biological physics
Countries involved: Switzerland
Call: Call for proposals for ERC Proof of Concept Grant (ERC-2020-PoC, 2020)
Start date: 2020-09-01
End date: 2022-02-28
Duration (months): 18.0
Total project cost (EUR): 150000.0
EU funding (EUR): 150000.0
Participants: 1
Project status: CLOSED

The technology for the growth of thin oxide films has reached the same level of atomic control as in the case of semiconductors. Yet, in contrast to semiconductors, oxides exhibit novel phases and functionalities like magnetic or electric order or dramatic changes of electrical resistance. One such functionality is the spontaneous formation of an electric polarization, called ferroelectricity, that is considered as basis for novel types of smart devices or energy-efficient functional components in CMOS devices. The infatuating technological potential of ferroelectric oxide heterostructures drives materials scientists to master their synthesis, understanding and control. Optimizing the ferroelectric performance is tedious, however. From the post-growth invasive and destructive analysis of prototypes, conclusions are drawn on the optimum heterostructure design and growth parameters. Monitoring the heterostructures while they are assembled in the growth chamber would provide immediate and direct feedback on the relation between growth and function and could thus immensely speed up the iterative process. The present lack of the according technique poses a restricting market factor. Here we will use a laseroptical process called second harmonic generation (SHG – doubling of the laser frequency) to track the emergence of the ferroelectric state directly, during the assembly of the oxide heterostructure in the growth chamber. Probing occurs in situ, non-invasively and non-destructively throughout the deposition process. With our Proof of Concept proposal POLARIS, we will cast such "in-situ SHG" into the prototype for a marketable system that is simply flanged onto the growth chamber. It is a technique with a high robustness against the sometimes harsh environmental conditions of a lab or, later on, a factory hall. Its ease of use will be similar to that a laser pointer.

Keywords: oxide electronics,thin film heterostructures,optical second harmonic generation,in situ,materials,processing diagnostics,ferroelectricity,electric polarization,pulsed laser deposition,laser