Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : MARIPOLDATA (Horizon 2020, appel 2018, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : University of Vienna — Pays : Autriche — Rôle : PI
Acronyme de l'entité : Univie
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1391932.0 €

In order to protect marine biodiversity and ensure that benefits are equally shared, the UN General Assembly has decided to develop a new legally binding treaty under the United Nations Convention on the Law of the Sea. Marine biodiversity data will play a central role: Firstly, in supporting intergovernmental efforts to identify, protect and monitor marine biodiversity. Secondly, in informing governments interested in particular aspects of marine biodiversity, including its economic use and its contribution to biosecurity. In examining how this data are represented and used, this project will create a novel understanding of the materiality of science-policy interrelations and identify new forms of power in global environmental politics as well as develop the methodologies to do so. This is crucial, because the capacities to develop and use data infrastructures are unequally distributed among countries and global initiatives for data sharing are significantly challenged by conflicting perceptions of who benefits from marine biodiversity research. Despite broad recognition of these challenges within natural science communities the political aspects of marine biodiversity data remain understudied. Academic debates tend to neglect the role of international politics in legitimising and authorising scientific concepts, data sources and criteria and how this influences national monitoring priorities. The central objective of MARIPOLDATA is to overcome these shortcomings by developing and applying a new multiscale methodology for grounding the analysis of science-policy interrelations in empirical research. An interdisciplinary team, led by the PI, will collect and analyse data across different policy-levels and spatial scales by combining 1) ethnographic studies at intergovernmental negotiation sites with 2) a comparative analysis of national biodiversity monitoring policies and practices and 3) bibliometric and social network analyses and oral history interviews for mapping marine biodiversity science.

Mots-clés : global environmental politics,marine biodiversity monitoring,data politics,convention on the law of the sea  unclos,convention on biological diversity  cbd,science  policy interrelations
Projet ERC : ABIONYS (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Aalto University — Pays : Finlande — Rôle : Partner
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 288667.0 €

In order to tackle some of the prime societal challenges of this century, science has to urgently provide effective tools addressing the redesign of chemical value chains through the exploitation of novel, bio-based raw materials, and the discovery and implementation of more resource-efficient production platforms. Nature will inevitably play a pivotal role in the imminent transformation of industrial strategies, and the recent bioeconomy approaches can only be regarded as initial step towards a sustainable future. Operating at the interface between chemistry and life sciences, my ABIONYS will fundamentally challenge the widely held distinction separating chemical from biosynthesis, and will deliver the first proof-of-concept where abiotic reactions act as productive puzzle pieces in biosynthetic arrangements. On the basis of our previous ground-breaking discoveries on artificial enzyme functions, I will create a significantly extended toolbox of biocatalysis modules by applying protein-based interpretations of synthetically crucial but non-natural reactions i.e. transformations that are in no way biosynthetically encoded in living organisms. My research will exploit these tools in multi-enzyme cascades for the preparation of complex organic target structures, not only to highlight the great synthetic potential of these approaches, but also to lay the groundwork for in vivo implementations. Eventually, the knowledge gathered from enzyme discovery and cascade design will enable to create an unprecedented class of bioproduction systems, where the genetic incorporation of artificial enzyme functions into recombinant microbial host organisms will yield tailor-made cellular factories. Combining classical organic synthesis strategies with the power of modern biotechnology, ABIONYS is going to transform the way we synthesize complex and functional building blocks by allowing us to encode organic chemistry thinking into living production platforms.

Mots-clés : biocatalysis,enzyme cascades,synthetic biology,cellular factories,pathway design,stereochemistry,green chemistry
Projet ERC : CHROMTISOL (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : University of Pardubice — Pays : Rép. Tchèque — Rôle : PI
Acronyme de l'entité : UPA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1644380.0 €

In photovoltaics (PVs), a significant scientific and technological attention has been given to technologies that have the potential to boost the solar-to-electricity conversion efficiency and to power recently unpowerable devices and objects. The research of various solar cell concepts for diversified applications (building integrated PVs, powering mobile devices) has recently resulted in many innovations. However, designs and concepts of solar cells fulfilling stringent criteria of efficiency, stability, low prize, flexibility, transparency, tunable cell size, esthetics, are still lacking. Herein, the research focus is given to a new physical concept of a solar cell that explores extremely promising materials, yet unseen and unexplored in a joint device, whose combination may solve traditional solar cells drawbacks (carrier recombination, narrow light absorption). It features a high surface area interface (higher than any other known PVs concept) based on ordered anodic TiO2 nanotube arrays, homogenously infilled with nanolayers of high absorption coefficient crystalline chalcogenide or organic chromophores using different techniques, yet unexplored for this purpose. After addition of supporting constituents, a solid-state solar cell with an extremely large incident area for the solar light absorption and optimized electron pathways will be created. The CHROMTISOL solar cell concept bears a large potential to outperform existing thin film photovoltaic technologies and concepts due to unique combination of materials and their complementary properties. The project aims towards important scientific findings in highly interdisciplinary fields. Being extremely challenging and in the same time risky, it is based on feasible ideas and steps, that will result in exciting achievements. The principal investigator, Jan Macak, has an outstanding research profile in the field of self-organized anodic nanostructures and is an experienced researcher in the photovoltaic field

Mots-clés : nanotubes,solar cells,charge carrier separation
Projet ERC : GROGandGIN (Horizon 2020, appel 2016, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : Hungarian Academy of Sciences — Pays : Hongrie — Rôle : PI
Acronyme de l'entité : MTA
Type d'entité : Recherche
Financement ERC de l'entité : 1965340.0 €

In recent years there has been spectacular progress in studying growth in groups. A central result in this new area, obtained by Pyber-Szabo' (with a similar result proved by Breuillard-Green-Tao), shows that powers of generating subsets of finite simple groups of "bounded dimension" grow fast. Extending this Product Theorem Szabo' and the PI also proved a weaker version of a conjecture of Helfgott-Lindenstrauss. The Product Theorem has deep consequences in the study of groups, number theory and random walks. A central open question of the area is to remove the dependence on dimension in our Product Theorem. The PI formulated a new Conjecture, as a step forward. The way to further progress is via combining techniques from asymptotic group theory and probability theory. It is from this perspective that the current GROGandGIN proposal addresses issues concerning random walks. We examine how recent probabilistic arguments for random walks in the symmetric group may be transferred to matrix groups. While the first results in the subject of growth concern matrix groups we see an evolving theory of growth in permutation groups. This relies on earlier work of Babai and the PI which aims at finding proofs which do not use the Classification of Finite Simple Groups (CFSG). Similarly, Babai's famous Quasipolynomial Graph Isomorphism Algorithm builds on ideas from CFSG-free proofs due to him. The PI has recently removed CFSG from the analysis of Babai's algorithm. Our method goes "halfway" towards removing CFSG from proofs of growth results for permutation groups, currently a major open problem. The GROGandGIN initiative plans to improve various other parts of Babai's paper, working with several people who look at it from different angles, with an eye towards obtaining a Polynomial Graph Isomorphism algorithm. The GROGandGIN team will also study growth in Lie groups since the theory of random walks in Lie groups has been revitalised using analogues of our Product Theorem.
Projet ERC : SupraCarrier (Horizon 2020, appel 2016, Proof of concept grants)
Entité participante : Eindhoven University of Technology — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : TU/e
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

In recent years, macroscale drug delivery systems have been designed and investigated to spatiotemporally control the release of various bioactive agents and drugs at specific locations in the body. Many of these macroscale drug delivery vehicles are based on hydrogels. Depending on the location of application, and the temporal release kinetics, these hydrogel drug carriers need to be adjusted to the need of the application. Many of the currently available hydrogel systems do not have the capacity to be totally tuned. These systems: a) display a substantial initial burst release of the drug resulting in high drug doses at the initial phase; b) are difficult to apply (surgical implantation is necessary for various systems, thereby limiting their potential of easy/convenient application); c) provide only limited opportunities for chemical adjustment of their composition to improve key material properties such as their release kinetics and/or degradation rate. SupraCarrier aims to address these unmet needs by developing an injectable, supramolecular ureido-pyrimidinone (UPy) based hydrogel (UPy-hydrogel) drug delivery system that allows for local and sustained drug release. The unique selling points of this UPy-hydrogel system are: a) its demonstrated sustained drug release profile in vitro (with no initial burst release); b) its injectability due to its unique physicochemical properties (that allow for the development of clinical strategies using minimally invasive procedures); and c) its chemical structure and supramolecular assembly behaviour which allows for easy modification of either the UPy-hydrogel carrier or the drug (resulting in a versatile drug delivery platform). With SupraCarrier we aim to a) validate the commercial feasibility of a UPy-hydrogel as a novel local drug delivery system for controlled (and sustained) drug release; b) prioritise the medical applications where this system can be used; and c) provide a proof of concept for using a UPy-hydrogel

Mots-clés : supramolecular hydrogel,drug delivery system,injectable,controlled release,medical applications,cancer,peritoneum
Projet ERC : STARE (Horizon 2020, appel 2020, Proof of concept grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Technical University of Darmstadt — Pays : Allemagne — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

In recent years, the analysis of large data sets is becoming increasingly important in the fields of material science and engineering. There is a strong demand for real-time automated identification algorithms in electron microscopy (EM) for the analysis of atomic-structure, phases, and defects. Unfortunately, it is non-trivial to obtain or extract meaningful scientific information from raw EM output digital data. It requires a tedious process of filtering/fitting and the expertise of a seasoned microscopist. With the rapid development of information technology and computer science, automated computer-assisted analysis of electron microscopy images/data is becoming a reality. In the past decade, different techniques have been developed and applied to digital data analysis. Meanwhile, the rapid development of novel microscopy techniques and instrumentation, e.g. in situ/operando and pixelated detector-based techniques, require high-speed data execution and analysis. Currently, several groups worldwide are concentrating their efforts into implementing machine learning and deep learning algorithms for image/data analysis. However, this is still a very undeveloped direction in the field of electron microscopy for materials science, especially in Europe. According to the Digital Transformation Monitor, artificial intelligence-based technologies will play a major role in future economy. The ability to analyse levels of data that are beyond human comprehension will allow business to personalize experiences, customize products and services and identify growth opportunities with a speed and accuracy that has never been possible before. The objective of this PoC is to generate an innovative software package that enables the analysis of large sets of EM data (i) at high throughput with (ii) low costs, in (iii) a standardized approach and (iv) under operando conditions, based on advanced machine learning algorithms.

Mots-clés : machine learning,transmission electron microscopy,big data,peak finding algorithm
Projet ERC : HY-CAT (Horizon 2020, appel 2016, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Synthetic Chemistry and Materials
Entité participante : Board of the Swiss Federal Institutes of Technology — Pays : Suisse — Rôle : PI
Acronyme de l'entité : ETH Zurich
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1420648.0 €

In reimagining the world’s energy future, while researchers are seeking alternative ways to produce energy, our current dependence on fossil fuels requires us to capture and store the CO2 to prevent reaching unacceptable CO2 levels in the atmosphere. In this scenario, recycling CO2 by converting it into useful chemicals, such as fuels for transportation, represents an important research area as it will eventually lead to independence from fossil fuels and petroleum. While much progress has been made, this emerging field is challenged by huge technical and scientific questions. The intrinsic thermodynamic stability of the CO2 molecule, combined with slow multi-electron transfer kinetics, makes its reduction exceedingly energetically demanding. Hy-Cat aims to develop novel material platforms to investigate different chemical paths that promote electrochemical CO2 reduction and direct product selectivity. We will synthesize hybrid materials comprising atomically defined CO2 sorbents and nanocrystalline CO2 catalysts intimately bound in a single integrated system. Three different classes of hybrids, each characterized by one specific absorption/pre-activation mechanism, will allow to investigate the effect of each mechanism on the catalyst activity. A key component of the research will be to develop synthetic schemes to access these multifunctional systems with an unprecedented level of control across multiple lengthscales. This control and the intrinsic tunability of the chosen building blocks will allow us to methodically compare structure and activity, so to determine the design principles upon which better catalysts can be made. We will argue that this understanding is required to remove the main bottlenecks towards efficient and selective catalysts to convert CO2 into useful products, such hydrocarbons. Hy-Cat is highly multidisciplinary and its scientific outcome will positively impact several other research fields in chemistry, materials science and engineering.
Projet ERC : HEATforecast (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : Board of the Swiss Federal Institutes of Technology — Pays : Suisse — Rôle : Partner
Acronyme de l'entité : ETH Zurich
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 743724.0 €

In summer 2018, a devastating heat wave affected the entire Northern Hemisphere. Climate change projections indicate that the severity and frequency of such heat waves will further increase over the next decades. At the same time, models remain unable to predict heat waves at lead times of a few weeks – a crucial planning timescale. The poor prediction skill at timescales of weeks to months is to a large extent due to an incomplete understanding of the underlying physical drivers of heat waves. In particular, the atmospheric fluid dynamics responsible for heat waves and their prediction are not sufficiently understood and tend to be biased in models. The seasonal cycle further modulates the drivers and predictability of heat waves. Climate change projections disagree on the changes in atmospheric dynamics responsible for heat waves. The proposed research takes an unconventional path to address these open questions by building a process-based hierarchy of prediction systems ranging from a dry dynamical core to a prediction system using full physics. This hierarchy approach is novel for prediction systems. By systematically adding processes to the model, the relative contribution of atmospheric dynamics and surface drivers for heat waves and their predictability can be estimated throughout the seasonal cycle and for the projected changes in heat waves with climate change. While solving a fundamental question in atmospheric fluid dynamics, the proposed research aims to significantly extend the warning horizon and thereby minimize the societal consequences for future heat waves, which are expected to increase in frequency but so far remain unpredictable. This project combines the experience and strengths of the PI in atmospheric dynamics, predictability, and their application in a timely manner by increasing the connections between the dynamics and predictability communities that will benefit the study of atmospheric fluid dynamics and predictability beyond heat waves.

Mots-clés : heat wave,atmospheric dynamics,jet stream,simplified model,model hierarchy,teleconnection
Projet ERC : COSMOS (Horizon 2020, appel 2015, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Mathematics
Entité participante : KU Leuven — Pays : Belgique — Rôle : PI
Acronyme de l'entité : KUL
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2250000.0 €

In survival analysis investigators are interested in modeling and analysing the time until an event happens. It often happens that the available data are right censored, which means that only a lower bound of the time of interest is observed. This feature complicates substantially the statistical analysis of this kind of data. The aim of this project is to solve a number of open problems related to time-to-event data, that would represent a major step forward in the area of survival analysis. The project has three objectives: [1] Cure models take into account that a certain fraction of the subjects under study will never experience the event of interest. Because of the complex nature of these models, many problems are still open and rigorous theory is rather scarce in this area. Our goal is to fill this gap, which will be a challenging but important task. [2] Copulas are nowadays widespread in many areas in statistics. However, they can contribute more substantially to resolving a number of the outstanding issues in survival analysis, such as in quantile regression and dependent censoring. Finding answers to these open questions, would open up new horizons for a wide variety of problems. [3] We wish to develop new methods for doing correct inference in some of the common models in survival analysis in the presence of endogeneity or measurement errors. The present methodology has serious shortcomings, and we would like to propose, develop and validate new methods, that would be a major breakthrough if successful. The above objectives will be achieved by using mostly semiparametric models. The development of mathematical properties under these models is often a challenging task, as complex tools from the theory on empirical processes and semiparametric efficiency are required. The project will therefore require an innovative combination of highly complex mathematical skills and cutting edge results from modern theory for semiparametric models.

Mots-clés : survival analysis,cure models,copulas,quantile regression,dependent censoring,measurement errors,endogeneity,asymptotic theory,semiparametric inference
Projet ERC : iMTFM (Horizon 2020, appel 2019, Proof of concept grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Institutions, Governance and Legal Systems
Entité participante : Delft University of Technology — Pays : Pays-Bas — Rôle : PI
Acronyme de l'entité : TU Delft
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

In sustainable transport systems, the role of multi-modal trips is increasingly important. Access, egress, and transferring within or between modes is an important source of trip disutility. The transfer experience needs to be substantially improved for multi-modal trips to be able to compete with car travel. This proposal focusses on making transfers at multi-modal transfer hubs as efficient, reliable and safe for the traveller as possible within existing transfer hubs using advanced sensing and actuation technology, state-of-the-art control strategies and our knowledge of passenger behaviour. We valorise technical and scientific results of the ALLEGRO project and apply it to design and test a pedestrian traffic management system for multi-modal transfer hubs. At the basis of the system is a state-of-the-art real-time monitoring system, that will collect and combine data from different types of sensors to estimate, predict and diagnose the flows inside the hub. The state-of-the-art sensors provide data with different semantic properties, ensuring that a comprehensive estimate of the current situation can be provided. One of the unique elements is the integration of sentiment information, stemming from social data analytics developed in ALLEGRO. Using a multi-faceted diagnoses system allows identification of problems in terms of traveller comfort, efficiency, and safety. The models developed in ALLEGRO are used to provide short-term predictions based on real-time sensor data, which prevents myopic decision making based on the current situation in the transfer hub only. Off-line pilots have shown the feasibility of using predictive approaches to provide decision-makers with advice on which actions to take. We will further develop and validate these approaches by testing them in an integrated environment. The system will be at (at least) one hub that already has been outfitted with a large sensor base as part of the SMART Station program.

Mots-clés : crowd management,passenger flows,tranfsers,stations