Data-Gouv-ML/pcri-actions-marie-curie-participations-et-projets-laureats huggingface dataset:
Projet MATT (Horizon Europe, appel à projets 2021)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Mathematics
Statut du projet : CLOSED

MATT wants to investigate new multiscale mathematical problems where the evolution of fluctuating internal surfaces with respect to time and space has to be considered. Such evolutions happen at small, unobservable spatial scales, as the evolving surfaces are typically contact interfaces between microscopic material phases. A prime example is the modeling of mechanical microstructural changes in steel (e.g. Bainite formation from Austenite) under fast temperature changes (pointwise sensor measurements are here unavailable). Besides thermoelasticity, other real-world examples leading to the same class of mathematical problems include swelling of porous media, growth of tumors, and thawing of glaciers/permafrost (now, a global problem). As the scale heterogeneity renders numerical simulations impossible, one must identify simplified models that are able to accurately describe and predict the material behavior while still being simple enough to allow for fast numerical simulations within the expected physical range. The objectives of MATT are: (a) develop a general mathematical framework for rigorously connecting different scales crossed by free boundaries, (b) design multiscale numerical schemes to simulate and validate the produced models, (c) facilitate the Researcher a quick development towards scientific independence, (d) boost the Researcher's awareness of the role the applied mathematician must play in science, technology, and society. Mathematical homogenization (two-scale convergence/periodic unfolding) is the main working tool. Due to the inherent non-linearity of moving boundary problems, several new results regarding uniform estimates and compactness arguments will be established and used to ensure convergence. Multiscale numerical schemes will be designed and implemented in Python/FEniCS. Experimental data for the Bainite transformation from Austenite will be used to validate our findings.

Mots clés : homogenization theory, asymptotic analysis, mathematical modeling, termoelasticity, steel

Participants :
- Université de Karlstad (Suède) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 206887.68 €
Projet PACMAN (Horizon Europe, appel à projets 2021)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Chemistry
Statut du projet : CLOSED

This proposal aims to capitalize on my unique multi-disciplinary expertise in nanotechnology, tissue engineering, polymer/inorganic hybrids, and microsurgical techniques, while acquiring new proficiencies in the creation of breakable hybrid organosilica nanocapsules, mesoporous silica nanoparticles, and nanocomposite hydrogels. I am driven to tackle the unmet needs, opportunities, and challenges in the fields of nanocomposite hydrogel design and nervous system repair by combing my current expertise with these new proficiencies to innovate nanocomposite hydrogels that are environmentally and spatially actuable. Specifically, the goal of my proposal is to pioneer spatially distinct and environmentally responsive actuation of nanocomposite hydrogels in response to matrix metalloproteinase-9 (MMP-9) peptide cleavage, focusing on applications in peripheral nerve repair. In order to achieve this ambitious objective, I will develop MMP-9 degradable polyethylene glycol (PEG)/mesoporous silica nanoparticle composites and MMP-9 breakable organosilica nanocapsules through the incorporation of MMP-9 cleavable peptides into silica nanoparticle structures. MMP-9 can therefore be used to induce: 1) hydrogel degradation, 2) release of encapsulated therapeutics, and 3) capture of molecules by cleaved nanoparticle scavengers. This proposal details a highly interdisciplinary approach to create a plug-and-play nanocomposite hydrogel system to overcome obstacles needed to improve regeneration outcomes by combining neuroscience and microsurgical nerve repair with chemistry, drug delivery, biomaterials science, and tissue engineering. Finally, nanocomposite nerve guidance conduits will be tested in a peripheral nerve injury model to demonstrate in vivo MMP-9 actuation of these hydrogels.

Mots clés : mesoporous silica, organosilica, matrix metalloproteinase, peripheral nerve repair

Participants :
- Mario Negri Institute for Pharmacological Research (Italie) — Coordinator, beneficiary, Recherche, subvention : 172750.08 €
Projet NEXG-UV (Horizon Europe, appel à projets 2021)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Information Science and Engineering
Statut du projet : CLOSED

Information processing, sensing and life science technologies are currently subject to a revolution driven by integrated photonics that promises high sensitivities and low-power consumption. Photonics based on silicon and silicon nitride cover well the infrared and visible spectrum of light, respectively, and even allow for a low-cost co-integration with complementary metal-oxide semiconductors (CMOS) electronics. However, these technologies do not cover the UV part of the spectrum which is important for many emerging technologies such as biosensing or ion quantum computing. NEXG-UV will innovate a next generation UV-photonics platform based on Alumina, public which is not only transparent in the UV-spectrum and features a great power capacity but also is compatible with large-scale fabrication of the CMOS platforms. Contrary, to prior attempts we will realize alumina photonic devices with smooth sidewalls and surfaces. Like copper patterning of the CMOS back-end, we will make use of chemical mechanical polishing in combination with a dry-etched dielectric template and high-aspect ratio deposition techniques such as ALD. This will enable photonic elements with small-feature sizes, large scale aspect-ratios and especially smooth surface to supress Rayleigh scattering that normally dominates losses at shorts wavelengths. This will have a significant impact for instance in quantum computation on trapped ion qubit systems and in life sciences on label free sensing/detection of biomolecules.

Mots clés : integrated photonics, waveguides, alumina, silicon oxide, ald, ultraviolet, cmp

Participants :
- Institut de micro-électronique et composants (Belgique) — Coordinator, beneficiary, Recherche, subvention : 175920.0 €
Projet DyNeMo (Horizon Europe, appel à projets 2023)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Mathematics
Statut du projet : SIGNED

Networks are ubiquitous in the society, technology, biology and economy. Being able to perform accurate inference and prediction on such data is highly non-trivial due to their large size and complex dynamic behaviour. In many networks such as social ones, connectivity patterns and network structurechange dynamically. On the other hand, technological networks such as water systems have fixed structure but dynamic processes might take place on them appear, such as failures. DyNeMo is a cross-disciplinary project that aims to provide a novel, explainable yet scalable framework using statistical machine learning to model dynamic behavior on networks, with the goal to answer crucial scientific questions with social and environmental impact, influencing public policy. The project combines statistical tools that ensure flexibility, data adaptivity and interpretable parameters with Deep Learning tools ensuring efficiency and scalability. The methods will be used to answer two highly impactful scientific questions given access on unique datasets. The first is how companies worldwide can maximize their socio-environmental impact aligned with UN sustainable goal strategies. The second question is how to quantify and predict behaviour of cascading pipe failures on the water distribution systems in Cyprus. The results will detect patterns and perform prediction, thus influencing public policy in alignment to the EU strategic goals on monitoring water systems. The fellow and the supervisor have complementary expertise which in combination with the plan, the arrangements and environment provided by the host institution ensure a successful implementation of this novel and timely project as well as an effective dissemination and utilisation of the expected outcomes. The results of the proposed action are expected to have important conclusions on socio-environmental impact and can shape decision making and policy making in the EU and around the world.

Mots clés : statistical machine learning, complex network modelling, dynamic behaviour, latent structure, cascading effects

Participants :
- Université de Chypre (Chypre) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 164328.0 €
Projet FunSynVIGS (Horizon Europe, appel à projets 2023)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Life Sciences
Statut du projet : SIGNED

Plants are threatened by a plethora of pathogens, with fungi accounting for important crop and post-harvest losses worldwide. Despite widespread spraying of fungicides, losses keep rising in the current warming world and the increasing use of chemical (and toxic) fungicides favours the emergence of resistant fungi strains. Hence, there is an urgent need to develop a new generation of highly efficient and environmental-friendly fungicides for securing present and future food availability. Classic RNA interference (RNAi) tools based on double-stranded RNA have been used for inducing antifungal resistance in plants, despite their lack of high specificity. More modern and highly specific RNAi tools are based on artificial small RNAs (art-sRNAs). Art-sRNA tools have been optimized mainly for high efficacy and transgenic use but are not well adapted for GMO-free application and have not been used yet for inducing antifungal resistance in plants. FunSynVIGS seeks to develop a novel, GMO-free RNAi technology consisting in systemic, viral vector-based fungicides expressing art-sRNAs for plant immunization against pathogenic fungi. The specific objectives are: (1) The development and optimization of viral vector-based fungicides expressing art-sRNAs against Botrytis cinerea, the causal agent of the gray mold disease, in the model plant species Nicotiana benthamiana. (2) The application of viral vector-based fungicides for immunizing tomato plants against B. cinerea. Successful completion of these objectives will provide a novel class of mobile RNAi-based fungicides for inducing highly efficient antifungal resistance in crops.

Mots clés : rnai artificial small rnas pathogenic fungi next generation fungicides plant

Participants :
- Conseil supérieur de la recherche scientifique (Espagne) — Coordinator, beneficiary, Recherche, subvention : 181152.96 €
Projet GendMedHab (Horizon Europe, appel à projets 2024)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Social Sciences and Humanities
Statut du projet : SIGNED

The present project aims to reach a full comprehension of early modern medical encounter, exploring how gender differentiation played out at all its levels. In particular, the research establishes a comparison, which has not so far made, between male and female patients in terms of relationship with the physician, trust in ancient medical tradition, ability to negotiate cures, and impact on the outcomes of medical visit. The project also casts light on the women’s self-image as patients, investigating to what extent they participated in the diagnostic and therapeutic processes, and providing a detailed picture of their approach to health, illness and the body. In particular, the research focuses on a relevant case study – the imperial Habsburg family in the 16th and 17th centuries –, and relies on a broad variety of archival patient-centred sources, such as the letters of private character the male and female members of the family sent, and their correspondence with the attending doctors. The medical consultation papers physicians compiled for the family members will be also examined. To achieve the objectives mentioned above, both the social context of the Habsburg courts – political and cultural organisms –, and the factors which influenced the medical practice there – connections between the sovereign’s health and politics, the interdenominational conflicts of the period, the high social status of the Habsburg patients, the hierarchized family structure, and the family members’ different degree of autonomy – will be considered. By intertwining social history of medicine, gender history, history of material culture, and history of emotions, the research will unveil how medical practice impacted on the imperial family’s self-determination and identity both as a collective political body and as individuals. The project will be conducted at Istituto Storico Italo-Germanico (ISIG) of Fondazione Bruno Kessler of Trento (Italy), a European leading research institute

Mots clés : habsburg family court medicine early modern medical practice female patients patient doctor relationship

Participants :
- Fondazione Bruno Kessler (Italie) — Coordinator, beneficiary, Recherche, subvention : 209483.28 €
Projet RELIC (Horizon Europe, appel à projets 2021)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Information Science and Engineering
Statut du projet : CLOSED

Increasing energy and resource efficiency in industrial systems is key to decrease harmful emissions by 90% by 2050. Reaching the environmental targets requires a holistic approach to how resources and energy are delivered to the industry by means of distribution networks, such as heat networks, electricity networks, or gas transport networks. I will devise new control strategies that ensure robust operation of distribution networks while ensuring safety and satisfaction of environmental objectives.  The environmental performance of the whole system hinges on the performance of distribution networks. Optimal control of such networks is complex due to timescales, from milliseconds to ensure safe operation of pumps or generators, to days or months to include environmental goals, spatial complexity, uncertainty related to varying operating conditions, incomplete information available, and limited computational power. Existing control frameworks are usually application specific and have limited use in large-scale systems. In the project, I will advance theory in data analytics and optimisation, and build on my industrial experience to develop operating strategies for distribution networks that will enable safe implementation and reaching the environmental targets.  There is a potential in integrating machine learning in control design to overcome the complexity while satisfying safety constraints, as shown in robotics and automotive industry. However, IPCC indicated that "The key challenge for making an assessment of the industry sector is the diversity in practices, which results in uncertainty, lack of comparability, incompleteness, and quality of data available in the public domain on process and technology specific energy use and costs". The research question I will address in this project is if and how incorporating data-driven learning in design of control algorithms leads to improved environmental performance and safe operation of large-scale industrial networks.

Mots clés : model predictive control, robust control, optimisation under uncertainty, gas transport

Participants :
- École des mines et de la métallurgie de Cracovie (Pologne) — Partner, associated partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
- Université norvégienne de sciences et de technologie (Norvège) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 210911.04 €
Projet DeepIsaHOL (Horizon Europe, appel à projets 2022)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Information Science and Engineering
Statut du projet : SIGNED

Developing generic proof automation methods for interactive theorem provers (ITPs) is challenging but valuable. Applications range from performance improvements in the verification of safety-critical systems to the certification of famous, long, and hard-to-prove theorems. We propose using recently successful reinforcement learning (RL) algorithms to learn proof strategies employed in vast ITP libraries and use them as generic methods to automate the ITP proving process. Specifically, the project aims to create a tactic for one of the most popular and automated ITPs, Isabelle/HOL, and train the RL algorithms with theorems from the two largest Isabelle libraries: HOL-Library and the Archive of Formal Proofs (AFP). We identify three gaps in the state of the art that such an RL-and-Isabelle approach would close and describe the work packages that will achieve the corresponding three research objectives. The project will be done under the guidance of Dr Josef Urban, who holds a Distinguished Researcher position at the Czech Institute of Informatics, Robotics and Cybernetics (CIIRC) within the Czech Technical University (CTU) in Prague. Dr Urban and the CIIRC group are experts in integrating machine learning (ML) and interactive theorem proving. Their infrastructure and expertise targeted to ML and ITPs provide the best research environment for this project. Jonathan Julián Huerta y Munive, the researcher carrying out the project, complements this expertise with his Isabelle/HOL experience and knowledge of ITP applications.

Mots clés : deductive verification, reinforcement learning, interactive theorem provers

Participants :
- Université technique de Prague (Rép. Tchèque) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 166278.72 €
Projet GIANT (Horizon Europe, appel à projets 2023)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Mathematics
Statut du projet : SIGNED

In this proposal semi-integral points refer to notions of rational points on algebraic varieties that satisfy an integrality condition with respect to a weighted boundary divisor. They were first introduced by Campana and by Darmon. Campana points have recently risen to the attention of the number theory community thanks to a Manin type conjecture in the recent work of Pieropan, Smeets, Tanimoto and Várilly-Alvarado. Semi-integral points provide both an intermediate notion and a generalisation of the notions of rational and integral points, thereby unifying the two theories. This proposal concerns the existence of semi-integral points and the density of orbifold pairs in general families having semi-integral points. The aims of this proposal are to determine good upper bounds for the density of orbifold pairs in a general family that have semi-integral points (WP1) and to compute obstructions to the existence of semi-integral points (and hence to integral points) in key examples corresponding to long-lasting questions in number theory (WP2).  The approach will combine a variety of techniques from analytic number theory, algebraic geometry and arithmetic statistics. For (WP1), the experienced researcher and the supervisor will develop a criterion to detect local semi-integral points together with a sieve method to estimate the number of everywhere locally soluble varieties in the family. For (WP2), the research team will develop a Brauer-Manin obstruction theory for semi-integral points to compute failures of the integral Hasse principle in fundamental examples and handle classical Diophantine problems such as the existence of integral points on diagonal cubic surfaces and the non-existence of consecutive powerful numbers.

Mots clés : semi integral points, campana points, darmon points, rational points, integral points, hasse principle failures in families, hasse principle failures, brauer manin obstruction, large sieve

Participants :
- Institute of Mathematics and Informatics (Bulgarie) — Coordinator, beneficiary, Recherche, subvention : 120812.16 €
Projet POLYFUN (Horizon Europe, appel à projets 2021)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Chemistry
Statut du projet : SIGNED

We are in an era of demand for new, high-performance and sustainable plastics. The challenge is to counterbalance the actual need of society for disposable devices without compromising the sustainability of the overall production. Within the polymer field, polyolefins account for more than 50% of global plastics demand. However, their main drawback is represented by their hydrophobicity and their poor applicability in blending, adhesion and dyeing due to a lack of functional groups. In this regard, one fundamental challenge is to develop a new strategy that combines polymer synthesis and the incorporation of polar moieties into polyolefins. Within this context, POLYFUN seeks to deliver functional polyolefins by employing a single iron-based catalyst to perform two subsequent catalytic steps in a one-pot strategy. This approach, although being an important tool at the molecular level, has not yet found any application in the polymer field to access materials with intriguing and robust properties in one single process. From a scientific and technological perspective, POLYFUN intends to fabricate functionalized polymers with tunable and unique properties with an innovative approach in the field. This project is highly interdisciplinary, involving different research areas such as organic chemistry, catalysis, and material science. As such, it is envisioned that the development of this new strategy can generate breakthrough scientific papers, valuable discoveries and/or potential patents. This fellowship brings a two-fold transfer of knowledge: advanced techniques in organic chemistry to the host institution and material chemistry to the fellow. Overall, the project’s multidisciplinarity and intersectoral nature will broaden the fellow’s competencies and will place her in a competitive position for her next career move.

Mots clés : functional polyolefins, iron catalysis, one pot strategy, organometallic chemistry, sustainable polymers

Participants :
- Conseil national de la recherche (Italie) — Coordinator, beneficiary, Recherche, subvention : 233151.83 €