Data-Gouv-ML/pcri-actions-marie-curie-participations-et-projets-laureats huggingface dataset:
Projet ExTaSea (Horizon 2020, appel à projets 2018)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Environment and Geosciences
Statut du projet : CLOSED

Sea-level rise is a major societal concern, with potential impacts on population, infrastructure and coastal environments. Coastal defence and adaptation strategies are highly dependent upon our ability to understand and predict sea-level variability, including extreme sea levels. Quantifying the upper tail probability of sea level – i.e., the risk of extremes - is therefore of high socio-economic relevance. Current projections suggest a low probability of polar ice-sheet decay scenarios, but they cannot be ruled out. Such scenarios are poorly parameterized and/or associated with large uncertainties in current projections. The ExTaSea project will address two sources of uncertainty in current projections of future sea-level: (1) our understanding of the system and, (2) the degree to which we can simulate natural variability. ExTaSea will do this by producing well quantified natural bounds on both the rate and magnitude of sea level rise (objective 1) and probability distributions that include specific information on high-end extremes for global mean and regional sea levels (objective 2). These will contribute to our understanding of these sources of uncertainty and will form the basis for policy-relevant extreme sea-level scenarios (objective 3) that account for the dynamic response of the ice sheets to climate forcing. ExTaSea will produce statistical distributions by: (1) collating and quality checking already available geological data from past time intervals of the last 200,000 years that are useful analogues for future change; (2) novel statistical techniques (e.g., modified Bayesian partition modelling) and (3) modelling of solid Earth deformation processes (GIA) that will allow absolute magnitudes of sea levels to be determined. As the geological record integrates all processes, the statistical distributions on the natural bounds of sea-level will include the high-impact (extreme) tail associated with mass loss from the polar ice sheets.


Participants :
- University of York (Royaume-Uni) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 212933.76 €
Projet CLOSeR (Horizon 2020, appel à projets 2018)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Environment and Geosciences
Statut du projet : CLOSED

Sea-level rise is one of the most dreadful consequences of future climate change. To estimate the associated risk and prepare ourselves better for its implications, we need to identify the spatiotemporal characteristics of the contributors to sea-level change. Out of all the contributors, land water storage change is the most debated one because studies so far do not even agree on its sign. In the framework of CLOSeR (Contribution of Land water stOrage to Sea-level Rise), I will estimate the spatiotemporal characteristic of contribution from land water storage to sea-level rise between 2002 and 2020, in a robust statistical framework called BHM (Bayesian Hierarchical Modelling) with the help of: 1) multi-mission satellite altimetry data over oceans 2) a new data-driven leakage corrected GRACE product 3) steric information from ARGO floats and 4) a novel data-driven GIA model. Within this two-year fellowship, hosted at University of Bristol, supervised by Prof. J. L. Bamber, I will develop a data-driven leakage corrected GRACE product for global applications, develop a BHM to solve for land water storage contribution while closing the sea-level budget, and identify the regions that contribute significantly to sea-level rise. The approach is unique, global in scale, and will address a multi-disciplinary problem comprehensively. The proposed action will complement the goals of GlobalMass project at University of Bristol and will go beyond. CLOSeR will produce significant advances in understanding the role of land water storage in contemporary sea-level change that will help us better predict the future. For the first time, we will produce spatial and temporal hot-spots of land water storage contribution to sea-level rise using a cutting edge statistical tool for signal separation: Bayesian Hierarchical Modelling. In this process, the training received, collaborations established, and knowledge gained, will help in making me an independent Earth scientist in future.

Mots clés : sea level budget, grace satellite products, large scale hydrology, hydro geodesy, bayesian modelling

Participants :
- University of Bristol (Royaume-Uni) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 224933.76 €
Projet ARTIST (Horizon 2020, appel à projets 2020)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Environment and Geosciences
Statut du projet : SIGNED

Seasonal Forecasts are critical tools for early-warning decision support systems, that can help reduce the related risk associated with hot or cold weather and other events that can strongly affect a multitude of socio-economic sectors. Recent advances in both statistical approaches and numerical modeling have improved the skill of Seasonal Forecasts. However, especially in mid-latitudes, they are still affected by large uncertainties that make their application often complicated. The ARTIST project aims at improving our knowledge of climate predictability at the seasonal time-scale, focusing on the role of unexplored drivers, to finally enhance the performance of current prediction systems. This effort is meant to reduce uncertainties and make forecasts efficiently usable by regional met-services and private bodies. A statistical/dynamical hybrid model will be designed through the synthesis of (a) a cutting-edge dynamical Seasonal Prediction System and (b) a statistical model based on advanced Machine Learning (ML) techniques. Such a hybrid approach may become critical to improve climate forecasts, because it combines the theoretical foundation and interpretability of physical modeling with the power of Artificial Intelligence (AI), that can reveal unknown or disregarded spatio-temporal features. ARTIST will focus on seasonal prediction of temperature hot/cold extremes in Europe, but its scalable nature can make it applicable across a wide range of variables and geographical areas. Besides the employment of AI, a strength of the action stands in the use of local land surface predictors to instruct the empirical model. The fellowship, which includes a variety of training activities, will be mainly conducted at the Barcelona Supercomputing Centre (Spain), a world-renowned institute for climate predictions and applications. A secondment period is projected at the Max Planck Institute for BGC (Germany), prominent in land studies and ML employment in earth science.

Mots clés : seasonal prediction, ensemble machine learning, deep learning, land atmosphere interaction, land surface drivers, statistical model, dynamical model, hybrid forecast system

Participants :
- Barcelona Supercomputing Center (Espagne) — Coordinator, beneficiary, Recherche, subvention : 172932.48 €
Projet LISTEN (Horizon 2020, appel à projets 2017)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Environment and Geosciences
Statut du projet : CLOSED

Seasonal and decadal climate predictions are routinely carried out, and are widely used for their numerous socio-economic applications. The improvement of the forecast capabilities at these timescales is the focus of the international effort coordinated by the World Climate Research Programme. The strategy of LISTEN to contribute to this challenge is structured to have two stages. First, it aims at enhancing the transfer of observed information to the model during the initialisation of a forecast. This phase of the climate prediction process is of utmost importance, because it has been shown that a correct initialisation can improve the forecasts up to a few years ahead. However, the systematic errors of the models make this task challenging, because of the discrepancy between the observed and model mean climate. The main consequences are incorrect propagation of systems and a quick loss of the observed information. LISTEN will therefore implement innovative initialisation techniques. These are explicitly designed to tackle specific limitations detected in the methods currently in use. The new techniques will be tested at both seasonal and decadal timescales, and their performance will be compared to the standard methods. The second stage of the project consists in exploiting the data produced by the first stage for an in-depth assessment of the prediction skill, with a special focus over Europe. Large uncertainties remain in predicting events on regional scales, such as heat waves, droughts or heavy rain and snow. LISTEN will aim at a thorough assessment of the model strengths and weaknesses in predicting those events under different initialisation strategies. In particular, the sub-seasonal circulation and extreme weather events will be studied in the framework of circulation patterns, through the analysis of large-scale recurrent patterns of variability (weather regimes). The tools developed to compute these process-based metrics will be made publicly available.

Mots clés : seasonal prediction, decadal prediction, initialisation, model bias, extreme events, atmospheric regimes

Participants :
- National Research Council (Italie) — Coordinator, beneficiary, Recherche, subvention : 168277.2 €
Projet CyclesOfLife (Horizon 2020, appel à projets 2020)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Environment and Geosciences
Statut du projet : SIGNED

Seasonal fluctuations occur throughout the planet. Ecosystems and humans alike exhibit and depend crucially on species' phenology, the timing of seasonal biological activity. Climate change has been distorting the length of the warm ‘growing’ season (i.e. summer) around the world. The resulting 'phenological shifts' comprise the most dramatic and well-documented symptom of climate change. However, phenological research has focused on idiosyncratic case studies of shifts with proximate explanations. A unifying framework that ties together the disparate body of evidence with ecological and evolutionary principles has been lacking, making it difficult to understand the source of variations and forecast continued phenological shifts. CyclesOfLife will establish such a framework and make timely use of big data to test the framework at an ambitious, global scale. I will build the theoretical backbone based on a novel approach I developed recently, which leverages quantitative tools from life-history theory and evolutionary demography to calculate natural selection dynamics of phenological traits in cyclically fluctuating environments. Armed with the theoretical framework, I will utilize the gradient of growing season lengths that exists across the planet's latitudes as a 'natural experiment' to test the relationship between season length and phenology. I will analyze demographic and phenological big data now available openly online to inform, test, and refine this theoretical relationship. Lastly, with deepened understanding of how growing season length around the world shapes phenology, I will make predictions of how phenology might continue to change in the future under different climate change scenarios. Beyond meeting the research objectives, the fellowship will enable my training in eco-evolutionary theory and big data science at Oxford, both powerful and timely skills needed for my career development as a leading researcher in phenology and evolutionary demography.

Mots clés : life history, phenology, demography, seasons

Participants :
- University of Oxford (Royaume-Uni) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 212933.76 €
Projet MOCHA (Horizon 2020, appel à projets 2016)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Environment and Geosciences
Statut du projet : CLOSED

Secondary organic aerosol (SOA) accounts for a large fraction of atmospheric aerosol mass and can have significant effects on atmospheric composition, human health, and climate. However, considerable uncertainty exists on the chemical composition of SOA, as well as the sources and processes leading to its formation in the atmosphere. In this project state-of-the-art analytical techniques will be used to determine the molecular composition of secondary organic aerosols in ambient urban air and the identity of the precursor volatile organic compounds (VOCs). These field measurements will provide detailed information on VOCs and particle composition that can be linked to specific pollution sources, such as vehicle emissions and biomass burning. A detailed programme of simulation chamber experiments will also be conducted to investigate the formation of SOA from anthropogenic VOCs that most strongly influenced aerosol composition at the measurement site. The simulation chamber experiments will generate novel information on the reaction pathways for anthropogenic SOA formation that can be incorporated into atmospheric models dealing with both air quality and climate. Overall, the unique information obtained from this combined field and laboratory study is expected to contribute to a greatly improved understanding of the composition, sources and processes leading to organic aerosol formation in the atmosphere, and in turn, the impact of SOA on human health and climate. During this project, the Fellow will receive high quality training through research that will significantly improve scientific knowledge and provide a strong platform for building an independent research career. A wide range of complementary and transferable skills will also be developed to enhance future career prospects.

Mots clés : aerosol composition, air quality, simulation chambers

Participants :
- National University of Ireland (Irlande) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 187866.0 €
Projet BURK-6 (Horizon 2020, appel à projets 2014)
Action : Postdoctoral Fellowships - Global Fellowships
Panel : Life Sciences
Statut du projet : CLOSED

Secretion systems are evolved machineries enabling bacteria to deliver toxins and virulence factors, called effectors, into target cells to enable the onset of infectious diseases. Uncovering these effectors is essential for the understanding of bacterial pathogenesis and the establishment of appropriate therapeutic strategies to tackle infectious diseases. The recently identified Type Six Secretion System (T6SS) is conserved in environmental and pathogenic Gram-negative bacteria. The T6SS is an organelle structurally akin to an intracellular and membrane-bound contractile phage tail used for the delivery of toxins into prokaryotic and eukaryotic target cells. The importance of this system in the context of infection is highlighted by its ability to not only target eukaryotic cells during bacterial infection, but additionally to target other bacteria co-infecting mammalian and plant hosts. Despite the recent advances made in understanding the mechanisms underlying the T6SS dynamic, very little is known about the T6SS effectors repertoire used by life-threatening pathogens, such as Burkholderia pseudomallei, and for which, vaccine strategies are currently unavailable. In the proposed research, a combination of high-throughput and cross-disciplinary technologies will be used to uncover and characterise novel T6SS effectors in B. thailandensis, a surrogate organism closely related to B. pseudomallei. This will 1) identify new T6SS effectors targeting eukaryotic cells, using a reporter-based transposon screening and 2) identify novel T6SS toxins targeting prokaryotic cells, using a genome-wide saturation mutagenesis strategy. The outcomes of the proposed research will identify the key cellular stages hijacked by the T6SS during the infection of a host, while exposing potential new bacterial targets exploitable for the development of novel antimicrobial strategies.

Mots clés : type six secretion system  t6ss, effectors, virulence, burkholderia thailandensis and pseudomallei, antibacterial toxins, high throughput, reporter assay, translocation, tradis

Participants :
- University of Melbourne (Australie) — Partner, organization partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
- University of London (Royaume-Uni) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 276107.4 €
Projet POP1Seed (Horizon 2020, appel à projets 2020)
Action : Postdoctoral Fellowships - European Fellowships
Statut du projet : CLOSED

Seed physiological quality is pivotal to ensure rapid and homogeneous seedling establishment required for stable yield and is a major economic challenge for the seed industry. Besides germination, vigor and storability or longevity, seed physiological quality also includes seed dormancy, i.e. the seed physiological stage preventing germination even under favorable conditions. While seed dormancy has been widely studied at population level, little has been done at single seed level. However, homogeneous seed lot production is crucial in plant breeding. In seeds, dormancy is mainly regulated by DELAY OF GERMINATION 1 (DOG1), a major Quantitative Trait Locus controlling seed dormancy. DOG1 expression is highly regulated, including by alternative polyadenylation which resulted in the discovery of functional DOG1 protein variant. However, whether this major QTL also regulates individual seed dormancy variability is still unknown. Here, this project combines several cutting-edge technologies such as single seed RNA sequencing analysis and mass spectrometry imaging to Genome Wide Analysis and mutant characterization to unravel novel regulators of single seed dormancy in Arabidopsis thaliana, with the perspectives to be applied on rapeseed. While improving my career prospects and learning from the establishment of international collaborations between the host institute in Poland and the academic partner IBMP-CNRS in France, this project POP1Seed is timely, and responds to expectations of the European Framework programme for Research and Innovation.

Mots clés : seed dormancy, individual seed, mass spectrometry imaging, arabidopsis thaliana, brassica napus, metabolic, rna sequencing, genome wide analysis

Participants :
- Polish Academy of Sciences (Pologne) — Coordinator, beneficiary, Recherche, subvention : 137625.6 €
Projet RUMBA (Horizon 2020, appel à projets 2020)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Chemistry
Statut du projet : CLOSED

Selective recovery of Critical Raw Materials (CRMs) from underexploited mining side streams can contribute to reducing the European dependence on imports of these refractory metals. RUMBA: “Selective Recovery of CRMs (Nb, Ta, W) by Using MOFs Based Adsorbents” aims to improve the efficiency of conventional hydrometallurgical methods. These methods require the digestion of ores under strong acid or alkaline conditions and the recovery of CRM via liquid-liquid extraction. RUMBA innovative approach is based on the use of solid adsorbents known as Metal-Organic Frameworks (MOFs). MOFs are crystalline materials with porous and flexible frameworks. Their properties (porosity, flexibility, adsorption capacity, chemical and thermal stability) make them capable of extremely fast sorption with unprecedentedly high selectivity toward targeted ionic species. Despite their suitability for heavy metals capture, MOFs have never been used for recovering CRMs. RUMBA involves the first study of the interactions CRM-MOF to guide the design of frameworks with high affinity/selectivity toward target CRMs. Extraction experiments will be performed in samples from real mining streams. Additionally, the CRM-MOF systems will be studied for possible added-value applications of environmental relevance. The feasibility for succeeding in the scientific and training objectives pursued in RUMBA is supported by the strong expertise of the host institution (ICAMCyL) in advanced mining technologies and the solid background of the researcher in synthesis and characterization of MOFs. RUMBA offers the researcher the opportunity of resuming her research career through the exploration of a completely different application for materials she is familiar with. She will gain scientific skills in mineral processing, CRMs regulation, and catalysis. Furthermore, her profile will grow in IPR management and patent exploitation thanks to the supervisor expertise in mining industrial innovation.

Mots clés : critical raw materials, mining technology, extractive metallurgy

Participants :
- Fundacion icamcyl (Espagne) — Coordinator, beneficiary, Recherche, subvention : 259398.72 €
Projet SuprAtom (Horizon 2020, appel à projets 2019)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Physics
Statut du projet : CLOSED

Self-assembly of nanoparticles (NPs) offers a versatile platform for the design of novel materials with enhanced collective properties. A promising route to achieving tailored properties with NPs is to bring them together into superstructures called Supraparticles (SPs). The greatest potential for bringing forth diverse new properties comes from multicomponent SPs, in which multiple types of NPs are used in the SPs. I propose to use spherical confinement to first build SPs which I will then treat with cation exchange (CE), a powerful tool for synthesizing NPs with controlled structures. The goal is to establish a robust route to structuring multicomponent SPs in a controlled manner and enable the engineering of new SPs with optimal properties for applications ranging from catalysis to photovoltaics. A complete structural analysis of cation exchanged (CE-ed) SPs in 3D is essential as it will reveal the CE process in SPs. I will develop innovative quantitative 3D electron microscopy (EM) techniques to investigate the dynamics of the structural evolution of CE-ed SPs on the single NP level, providing insights into how to achieve optimal properties. Optimization of sample support and development of fast multimode electron tomography will make this possible by eliminate beam damage. Liquid tomography will allow me to fully understand the 3D structures of CE-ed SPs under realistic conditions. By combining in-situ heating and fast multimode electron tomography, I will decipher the mechanism of heat-induced intra- and inter- particle CE in SPs. My program will enable me to understand the interplay between NP shape, stacking and heating on the resulting SP structures. This program will be the start of a completely new research line in the fields of both colloidal science and 3D characterization. The outcome will boost the possibilities for the design and application of functional materials as well as push the limits of 3D EM techniques.

Mots clés : electron tomography, self assembly

Participants :
- University of Antwerp (Belgique) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 178320.0 €