Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Plant biomass for high-performing sustainable materials (XYLAN-2.0)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Reintegration panel
Action type: Marie Skłodowska-Curie actions
Panel: Life Sciences
Countries involved: Denmark
Call: Individual Fellowships (H2020-MSCA-IF-2018, 2018)
Start date: 2019-04-08
End date: 2021-09-08
Duration (months): 24.0
Total project cost (EUR): 207312.0
EU funding (EUR): 207312.0
Participants: 1
Project status: CLOSED

Plant biomass is an important renewable resource. Elucidating its compositional complexity imposes a fundamental limit to its application. My research shows that cellulose organization in the secondary cell wall is governed by the xylan component far more than currently thought. Determining the full function of xylan in cellulosic material is the challenge addressed by XYLAN-2.0. To address this challenge, I will characterize cellulose fibril agglomeration as it occurs under different conditions involving xylans with known changes in primary structure. Firstly, I will map changes in cellulose fibril patterning in a large number of xylan mutants of the plant Arabidopsis. Secondly, I will systematically investigate nanocellulose:xylan composites made from Arabidopsis wildtype and mutant xylan preparations. Thirdly, I will attempt to produce “recombinant” xylan with well-defined primary structure specifications and use it to produce nanocellulose:xylan composites. These experiments will provide unprecedented insight into the function of xylan and aim to develop a mechanistic and quantitative model for how xylan modulates cellulose agglomeration. These new insights will have an enormous impact on biomass crop improvement, processing and application, particularly on biomass applied for cellulose-based high-performance materials. The experience of my supervisor Prof. Ulvskov in nanocellulose and material science combined with my experience in xylan biochemistry and biosynthesis make an ideal environment for carrying out this project and establishing myself as a leading independent multidisciplinary researcher.

Keywords: secondary plant cell wall,cellulose,nanocellulose,xylan,glucuronoxylan,hemicellulose,composites,scanning electron microscopy,arabidopsis,chlamydomonas,algae,fermentation
Cyprus Center for Algorithmic Transparency (CyCAT)

Framework: Horizon 2020 — Spreading excellence and widening participation
Programme: Widening participation and spreading excellence
Theme: Improved access to Excellence
Action type: Coordination and support actions
Countries involved: Italy,Israel,Cyprus,United Kingdom,United Kingdom
Call: Twinning (H2020-WIDESPREAD-05-2017-Twinning, 2017)
Start date: 2018-10-01
End date: 2021-12-31
Duration (months): 39.0
Total project cost (EUR): 999965.0
EU funding (EUR): 999965.0
Participants: 5
Project status: CLOSED

Despite strong indications that Cyprus is now in stable economic recovery, and that the island boasts one of the highest rates of tertiary educational attainment in Europe, the picture painted regarding the level of digital skills among citizens is a dismal one. In the 2017 Digital Economy and Society Index, Cyprus ranked 22nd within the EU, and belongs to the cluster of low performing countries. These findings are particularly worrying in light of the rapid changes taking place in the information landscape, such as the global consolidation of networked information services, and the rise of proprietary algorithmic processes that mediate citizens’ access to information and opportunities. The project will establish a network of researchers with key expertise in the areas of informatics and algorithmic biases, enhancing the Cyprus Center for Algorithmic Transparency, a research center at the Open University of Cyprus (OUC). CyCAT will become the regional expert in issues of information access in light of algorithmic gatekeeping, working with local authorities, librarians and educators to promote algorithmic transparency and enhanced digital literacy skills. Expected outcomes are 1) raising the international profile of the OUC researchers as evidenced by invitations to serve on committees, editorial boards, invited talks; 2) increased output in terms of number and quality of publications of OUC researchers; 3) attraction of additional external research funds to sustain CyCAT. Expected broader impacts include: 1) creation of digital artifacts through showcase projects that are relevant to Cypriots’ information needs and that can help raise awareness of algorithmic bias in the systems they routinely use; 2) educational materials based on CyCAT scientific outputs, that can be used by librarians and teachers; 3) increased opportunities for young researchers engaged with CyCAT; 4) increased dialogue on the importance of digital skills.

Keywords: algorithmic transparency and accountability,information access and retrieval,personalization
Probing the bypassability of genetic constraints in drug-resistance enzymes (PROBYDE)

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-2020, 2020)
Start date: 2021-07-01
End date: 2023-06-30
Duration (months): 24.0
Total project cost (EUR): 160932.48
EU funding (EUR): 160932.48
Participants: 1
Project status: SIGNED

The fast evolution of bacterial pathogens towards antibiotic resistance is estimated by 2050 to be killing 10 million people every year. Consequently, much interest is being directed towards finding ways to curb or even arrest this evolutionary process. Of note, sequencing efforts are revealing that many of the genetic changes that drive resistance evolution are often repeatable. Understanding what drives this repeatability is of foremost importance if we ever want to develop interventions aimed at anticipating and preventing the evolution of undesirable variants. Here, I will aim at advancing our understanding of evolutionary repeatability in several clinically-relevant, drug-resistance enzymes spanning a range of GC compositions. I will use this relevant model system to empirically test recent predictions on the roles of mutation bias and GC content in shaping mutational pathways. To this end, I will conduct high-throughput 'in vitro' Directed Evolution experiments to explore the potential adaptive paths available via single step mutations among the drug-resistance enzymes. Next, I will compare these 'in vitro' predictions with the outcomes of highly-parallel antibiotic adaptation experiments conducted with bacterial strains with strong mutation biases (e.g., mutators) carrying the same panel of enzymes. By producing important insights into some of the key determinants of evolutionary repeatability, PROBYDE aspires to form a knowledge base that may help harness evolution not only in bacterial pathogens, but also in other human-relevant systems such as cancer, crop pests and industrial microbes.

Keywords: epistasis,mutation bias,evolutionary genetics,experimental evolution,antimicrobial resistance
Verification and Language Theory (VERLAN)

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: Netherlands
Call: Individual Fellowships (H2020-MSCA-IF-2020, 2020)
Start date: 2021-09-01
End date: 2024-08-26
Duration (months): 24.0
Total project cost (EUR): 175572.48
EU funding (EUR): 175572.48
Participants: 1
Project status: SIGNED

Limitations to a programming language can be helpful in producing more readable and maintainable software. In addition, the semantic domain associated with such a limited fragment may admit more efficient algorithms for verification and analysis. A proper understanding of such algorithms starts with a characterisation of this limited semantic domain. Thus, to describe and reason about the possible behaviour of a program, we need methods to formalise the description the domain. VERLAN proposes to study the semantic domains of fragments of programming languages through the lens of formal language theory. I will study and inter-relate three mathematical frameworks to classify formal languages (varieties, covarieties, and logics). I will derive new algorithms for three fundamental problems in program verification: refactoring, equivalence checking, and learning, optimized for the class of formal languages. The Institute for Logic, Language and Computation (ILLC) is the prime location for this fellowship because of the expert knowledge already present there, such as my supervisor Dr. Nick Bezhanishvili, as well as my second supervisor, Prof. Yde Venema. My own research experience in program verification and formal languages aligns very well with the goals of this proposal. I will also collaborate with several researchers outside the ILLC, through planned research visits to institutions in France, Germany, and Canada. This proposal also includes a number of plans designed to enhance my career prospects as a researcher as well as my integration with the ILLC. These plans have been fine-tuned so as to spread the workload across the full two years of the fellowship, and include extensive progress monitoring. They include professional training for soft skills, grant writing, and teaching certification, as well as a strategy for outreach, and several activities aimed at dissemination of the research.

Keywords: universal co algebra,formal language theory,program verification,exact inference
Density Modulated Silicon Anode for Lithium Ion Batteries (DEMAND)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Türkiye
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2019)
Start date: 2019-12-01
End date: 2020-05-31
Duration (months): 6.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

The overall lithium ion battery market is expected to grow from $37.4B in 2018 to $100.4B by 2025, at a CAGR of 17.1%. The growth of this market is being fuelled by increasing demand for smart devices and other industrial goods. One of the major cost centers for lithium-ion batteries is anode material and it accounts for ~18% of the battery cell. The state-of-the-art anodes suffer from one or more of these problems: • Limited Li storage capacity, • Large irreversible capacity loss, • Low charge/discharge rate capability, • Poor capacity retention upon the charge/discharge cycling. Silicon(Si) is the best material in nature to create LIB anodes of highest energy density with a theoretical limit of 4200 mAh/g charge capacity. However, Si faces asubstantial challenge: Swelling due to extensive volumetric expansion during charging, which leads to breaking of the Si anode and short battery lifetimes. Usage of graphene (hugely recognized as a “wonder material”) can improve such battery attributes to a certain extent with a charge capacity of ~740 mAh/g; however, it adds to battery fabrication costs due its extremely high price. In addition, fabrication costs of the Li-ion batteries with current technologies is reaching to $300/kWh, while EC is targeting 75 €/kWh and U.S. is targeting $80/kWh by 2030. Therefore, there is an urgent need for high energy density and low cost batteries that will enable our electric future in this market. As IFGROUP, we are offering the “DEMAND” technology that allows us to modulate the dennsity of the silicon anode making it flexible and durable. The prominent advantages of DEMAND silicon anodes will be: • Provides high energy density with improved battery life (~10X); • The overall cost of the battery reduces (below $100/kWh); • Can be easily transferred to commercial battery manufacturing (scalable). DEMAND will allow us to generate €156.4M accumulated profit, 143 employees and 1.44 ROI by 2025 and €186.5M EBITDA within the same period

Keywords: lithium ion batteries,high energy density batteries,100 silicon anodes,patented technology,density modulated silicon anode,nanostructured silicon thin film
Skilled Migrant Adjustment to Career Transitions (SMACT)

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: Social Sciences and Humanities
Countries involved: Switzerland,Switzerland
Call: Individual Fellowships (H2020-MSCA-IF-2019, 2019)
Start date: 2021-09-01
End date: 2023-08-31
Duration (months): 24.0
Total project cost (EUR): 191149.44
EU funding (EUR): 191149.44
Participants: 2
Project status: SIGNED

The Skilled Migrant Adjustment to Career Transitions (SMACT) Project aims to research adjustment processes in skilled migrant workers in Switzerland. A sample of skilled multi-national migrant workers will be studied to examine their adjustment to a single host country (i.e., Switzerland) in terms of their work-related outcomes. The use of a single host country will allow better control to be achieved for the cultural and institutional influences that could be considered specific to Switzerland. The current interdisciplinary study aims to integrate adjustment dimensions that were previously used in diverse fields, such as psychology, human resource management and intercultural relations research. In this mixed-method research design, variables were investigated using qualitative (i.e., semi-structured focus group interviews with female skilled migrants) and quantitative data collections (i.e., online survey with skilled migrants administered over three time-points). The inclusion of qualitative focus group interviews with skilled female migrants aims to rectify the existing under-representation of female migrants’ adjustment experiences in expatriate literature. The SMACT project has four main goals: 1) examine female expatriate adjustment processes, 2) examine differences in adjustment and work-related outcomes between AEs and SIEs, 3) examine personality predictors on adjustment processes for work outcomes, and 4) examine personality predictors on adjustment processes for non-work outcomes. This research study is in line with European 2020 Strategy flagship initiatives of fostering employment and reducing poverty and social exclusion, because these study results could inform human resource management and career counselling practices to help facilitate skilled migrant adjustment processes, to address an existing gender dynamic and ultimately help organisations to better utilise the skills of migrants.

Keywords: expatriate adjustment,skilled migrant groups,career transitions,female expatriate adjusment processes,personality predictors on adjustment processes for work and non work outcomes
ROle and impAct of Dust and clouds in the Martian Atmosphere: from lab to space (ROADMAP)

Framework: Horizon 2020 — Industrial Leadership
Programme: Space
Theme: Enabling exploitation of space data
Action type: Research and Innovation actions
Countries involved: Germany,Spain,Belgium,Denmark
Call: Scientific data exploitation (H2020-SPACE-2020, 2020)
Start date: 2020-11-01
End date: 2023-10-31
Duration (months): 36.0
Total project cost (EUR): 1486273.75
EU funding (EUR): 1480023.75
Participants: 4
Project status: SIGNED

The goal of the ROADMAP project is to better understand the role and impact of dust and clouds on the Martian atmosphere. Although dust is present throughout the Martian atmosphere, its abundance and physical properties are still poorly defined. Similarly the impact of dust on the composition, structure and dynamics of the atmosphere is only beginning to be addressed. Specifically, accurate knowledge of the characteristics of dust and ice clouds is crucial for the interpretation of the remote sensing observations, both in the infrared and the ultraviolet spectral regions. The ROADMAP project employs an integrated scientific approach including; laboratory simulations, modelling of specific phenomena, analysis of space data and Global Circulation Modelling. This will improve our vision of the Martian atmosphere and provide a new generation of high-level data, increasing the science return of the past and current missions to Mars as well as shaping future planetary missions. We will focus on two European missions which are still active around the Red Planet and in particular make use of the ExoMars TGO instrument NOMAD, which has been optimized for the detection of trace gases, dust and clouds in the Martian atmosphere. This project is therefore both timely and highly relevant to the SPACE-30-SCI-2020 call by addressing scientific data exploitation and the objective of increasing the science return of specific European space missions.

Keywords: european space missions to mars,remote sensing of atmosphere,aerosols,dust and clouds,laboratory measurements,retrieval techniques,global circulation model,composition and climatologies
Hybrid Machine Learning – Optimization techniques To Generate Structured Music Through Morphing And Fusion (MorpheuS)

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: 2015-06-01
End date: 2017-05-31
Duration (months): 24.0
Total project cost (EUR): 183454.8
EU funding (EUR): 183454.8
Participants: 1
Project status: CLOSED

State-of-the-art music generation systems (Continuator, OMax, Mimi) produce music that sounds good on a note-to-note level but lacks critical structure/direction necessary for long term coherence. To tackle this problem, we propose to generate compositions based on structural templates at varying hierarchical levels. Our novel approach deploys machine-learning methods in an optimization context to morph existing pieces into new ones and to fuse different styles. We aim to develop a framework that combines machine learning techniques that learn style, with a powerful optimization method, the variable neighbourhood search (VNS) algorithm, for generating music. This approach allows the learned model to incorporate a wide variety of constraints, including those for preserving long term coherence and structure. It promises to effect a step-change in automatic music generation by moving the field in the new direction of generating structured music using hybrid machine learning-optimization techniques. The applicant is an operations researcher-musician, ideal for this work. A first step combines her VNS music generation algorithm with machine learning methods to ensure proper style evaluation. In previous work, the applicant has shown that VNS outperforms genetic algorithms when generating counterpoint with a rule-based objective function. In a preliminary study, the applicant has demonstrated the effectiveness of using machine learning techniques as evaluation metrics for optimisation methods. The applicant has extensive web development experience; to reach the widest possible audience, the resulting system will be made available in an interactive website where users can morph and fuse musical pieces. This work is situated in the area of digital media, with a European consumer expenditure of over €33 billion in 2011, projected to increase. Music generation in digital music has direct applications in game music, interactive arts, and stock-music for advertising/videos.

Keywords: music,computer aided composition,metaheuristics,data mining,machine learning
Scanning SQUID view of emergent states at interfaces (SENSQUID)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Condensed matter physics
Panel description: Structure, electronic properties, fluids, nanosciences, biological physics
Countries involved: Israel
Call: ERC Starting Grant (ERC-2014-STG, 2014)
Start date: 2015-04-01
End date: 2020-03-31
Duration (months): 60.0
Total project cost (EUR): 1499778.0
EU funding (EUR): 1499778.0
Participants: 1
Project status: CLOSED

The emergence of novel states of matter in low-dimensional systems is one of the most intriguing current topics in condensed matter physics. For instance, interfaces between certain non-magnetic insulating oxides were shown to give rise to surprising metallic, superconducting, and magnetic states, which are still far from being understood. I have recently demonstrated in LaAlO3/SrTiO3 that there is a strong influence of the constituent’s structure on the interface conductivity (quasi-1D rather than 2D) and sub-micron ferromagnetic patches that coexist with inhomogeneous superconductivity. However, the origin of the interface magnetism, its relation to transport properties, and the mechanisms that control the different interface states are yet to be understood. I believe that the only way to fully understand the electronic and magnetic behavior in reduced dimensions is by combining extremely sensitive, non-invasive, local techniques, but such characterization tools are lacking. The aim of this project is to investigate the rich phenomena that appear at transition metal oxides interfaces, starting with LaAlO3/SrTiO3 as a model system, and expanding to other ground states (e.g. multiferroics, quantum materials, metal-insulator), as well as to other low-dimensional systems, including 2D-superconductors, topological insulators and carbon nanotube coils. To this end, I will develop an advanced scanning SQUID technology for higher temperatures, improved resolution, simultaneous mapping of orthogonal properties, and high throughput. By detecting nano-magnetism, traces of superconductivity, and non-invasively mapping the path of current flow, our tool will detect new states of matter, follow their interactions, correlations, and response to modulation in the local potential with extreme sensitivity. Our results will open up access to fundamental physics in atomically engineered materials, and to the control of their properties for use in next generation nanoelectronics.

Keywords: complex oxide interfaces