Data-Gouv-ML/pcri-actions-marie-curie-participations-et-projets-laureats huggingface dataset:
Projet PhyPPL (Horizon 2020, appel à projets 2019)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Life Sciences
Statut du projet : CLOSED

Statistical analysis of phylogenetic models is one of the most active areas of research in computational biology today with wide applications in the Theory of Evolution, epidemiology, forensics, etc. Current phylogenetic software packages limit the user to the set of phylogenetic models and inference strategies that have been pre-programmed in the tool. Inference under certain important phylogenetic models is very difficult with the Markov chain Monte-Carlo strategy implemented in current packages for phylogenetic analysis. The new paradigm of probabilistic programming, coming from computational statistics and theoretical computer science, solves the model expression problem and enables the user to implement novel inference methods. We utilize probabilistic programming to automatically generate Sequential Monte Carlo (SMC) inference machinery for MCMC-hard problems in phylogentics. SMC algorithms may be more efficient, provide unbiased solutions, and provide likelihoods estimates for model comparison. The goal of the proposed research is to carry out some of the first applications of probabilistic programming to real-world problems of empirical interest in evolutionary biology. The objectives are (1) to design and implement statistical inference machinery for complex diversification models with variable tree topology and a trait-dependent branching process under probabilistic programming, (2) to do a pilot study on the applicability of this inference machinery by studying the effect of the orogeny of the Andes on Neotropical biodiversity, and (3) contribute to the design and implementation of a novel probabilistic programming language for phylogenetics, TreePPL, by utilizing the insights gained from (1) and (2). We also propose dissemination and communication measures that target scientists and the general public throughout Europe and in particular new and aspiring EU member states.

Mots clés : bayesian statistics, probabilistic programming, phylogenetics, artificial intelligence, theory of evolution, biogeography, genomics, statistical inference, compilers

Participants :
- Swedish Museum of Natural History (Suède) — Coordinator, beneficiary, Recherche, subvention : 203852.16 €
Projet LearningEmotions (Horizon 2020, appel à projets 2019)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Social Sciences and Humanities
Statut du projet : CLOSED

Statistical learning refers to the ability to learn through the discovery of patterns and structures. I propose to investigate emotion recognition using a statistical learning perspective in order to understand (i) why some emotions are harder to recognise than others; and (ii) why individuals with autism spectrum disorder (ASD individuals) have more difficulty recognising emotions than neurotypicals (i.e., individuals without autism). I argue that part of the difficulty in recognising certain emotions lies in how reliable or consistent the auditory and visual cues are in signalling the emotion. That is, if particular cues consistently signal or have a high probability of signalling an emotion (e.g., 'happy' is consistently signaled by squinty eyes and grin/smile), then that emotion would be easier to recognise than emotions that are signalled by inconsistent cues (e.g., sarcasm may have varied expressions depending on the individual, context, etc. and so sarcasm would be more difficult to recognise). To investigate this, I will use an audio-visual emotion database that is currently under development to quantify the variability of cues across speakers in signalling the intended emotion. I propose that the difficulty ASD individuals have with recognising emotions lies in a general difficulty with consolidating probabilistic information. In terms of emotion recognition, this would manifest as a difficulty with making a correct inference of the intended emotion given particular cues, which vary in their probabilities in signalling the emotion. To investigate this hypothesis, I will conduct a behavioural and a neural experiment comparing ASD individuals with neurotypicals on probabilistic learning to determine whether group differences exist and whether probabilistic learning is related to emotion recognition. Outcomes of this project may inform intervention practices for ASD individuals and provide a general framework of understanding other ASD characteristics.

Mots clés : autism, statistical learning, probabilistic learning, emotion

Participants :
- University of Reading (Royaume-Uni) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 212933.76 €
Projet ML Potentials (Horizon 2020, appel à projets 2017)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Chemistry
Statut du projet : CLOSED

Statistical-learning approaches are emerging as powerful alternatives to direct approaches to solving the electronic Schrödinger equation for determining the energy and other properties of molecules. Despite the recent success of methods like deep neural networks, these methods are limited to relatively small molecules. The issue is that predicting long-range intermolecular interactions with machine learning requires sampling the vast diversity of chemical environments that occur on an extended length scale, leading to a combinatorial explosion in the amount of training data that is required. To solve this problem, the functional form of the long-range interactions is taken from physical models, but the parameters that enter those expressions (atomic charges/multipoles; induced charges/multipoles; van der Waals coefficients) are determined by combining physical insight with machine learning. In this model, machine learning is used only to predict short-range phenomena like the dependence of atomic charges/multipoles on the molecular structure and the dependence of induced atomic charges/multipoles on the local electric field. The resulting machine-learned physically-motivated atomistic intermolecular potentials are valid for molecules of any size, but only require training data from small- and medium-sized molecules. This development will provide molecular energies with the accuracy of quantum methods, at the computational cost of classical molecular mechanics approaches. This not only allows one to compute interaction energies for large molecules (e.g., the binding energy between a drug and a receptor), but allows the computational screening of molecules based on computed interaction energies. In addition to its transformative computational utility, this pioneering strategy—using physical insight to build a model, then using machine learning methods for the parameters in the model—can be extended to many other problems in chemistry, physics, and materials science.

Mots clés : quantum chemistry, molecular physics, physical chemistry

Participants :
- University of Luxembourg (Luxembourg) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 160800.0 €
Projet TurbDDT (Horizon 2020, appel à projets 2017)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Information Science and Engineering
Statut du projet : CLOSED

Statistics show that fires and explosions are the top cause of Business Interruption loss. Despite increasingly stringent safety measures, explosions continue to occur with higher frequency and consequences especially when Deflagration to Detonation Transition (DDT) occurs. Flame acceleration (FA) and DDT involve complex physical and chemical processes. Current provisions for explosion safety design are based on mechanisms for explosives and insufficient to interpret the complex nature of gas explosions. Their use in safety design is problematic. DNS predictions have shown the importance of TF on FA and DDT in uniform mixtures. Such influences are likely to be even more profound in mixtures with concentration gradients and when obstacles are present. There lacks experimental and numerical investigations to shade light on this. Robust and efficient predictive techniques which can capture global safety features associated with FA and DDT as well as TF are also missing. TurbDDT aims to fill these knowledge gaps. It aims to predict FA and DDT in industrial scale explosions incorporating the turbulence effects. The specific scientific objectives include: 1. To gain insight of TF on FA and DDT in smooth channels/tubes with uniform mixtures and mixtures with concentration gradients using DNS; 2. To repeat the above in channels/tubes with obstacles; 3. To assess the capability of the compressible linear eddy model in large eddy simulations (CLEM-LES) for medium scale simulations and compressible reactive solver (CRS) for large scales; 4. To conduct large scale FA and DDT of practical scales and assess the resulting differences in the predicted likelihood of DDT and explosion impact on structures when the more efficient CRS approach is used; and to draw conclusions and guidelines on large scale FA and DDT predictions. 5. Foster a two-way transfer of knowledge between the ER and host; and 6. Disseminate and communicate TurbDDT results to wider audiences.

Mots clés : combustion, flame acceleration, deflagration to detonation transition, direct numerical simulation, large eddy simulation, mixtures with concentration gradients, turbulent fluctuations

Participants :
- University of Warwick (Royaume-Uni) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 195454.8 €
Projet Microsupercapacitor (Horizon 2020, appel à projets 2020)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Physics
Statut du projet : SIGNED

Stretchable microsupercapacitors (MSCs) can operate in multiple mechanical distortions including stretching, bending, twisting, and compression. These mechanical adaptabilities are essential for powering wearable electronics and implantable biomedical devices. Recent progress in the stretchable supercapacitor field primarily emphasizes on the electrode materials and their design concept. In the search of outstanding electrode material, we have seen consumer-grade carbon-based MSC electrodes would be benefitted from a thin layer of diamond (sp3-hybridized carbon) coating as diamond possesses wide electrochemical (EC) potential window, a low and stable background current, and exceptional stability in a wide range of corrosive media. However, most of the growth techniques like chemical vapor deposition and atomic layer deposition support the diamond deposition on any solid hard substrate (such as Si, Mo, Ta, etc.), and also the gas phase growth temperature is too high to deposit diamond on any flexible substrate. On the other hand, recently, large scale, facile and one-step process for the production of flexible graphene-based porous nanomaterial called laser-induced graphene (LIG) has been investigated worldwide and regarded as a suitable platform to build EC energy storage devices. Therefore, we believe that in situ bonding between the flexible porous graphene networks with nanocrystalline diamond using a single-step lasing process, can provide a major breakthrough in the field of flexible wearable and portable electronic device technology. To utilize the dual advantage of both sp2 and sp3 bonded carbon, herein, we propose a simple strategy to synthesize nanostructured carbon hybrid where conductive nanodiamonds will be decorated between the flexible three-dimensional porous network of sp2 carbon (LIG). In the application scenario, the carbon hybrid then transferred to a transparent and stretchable silicone rubber substrate to obtain all-solid-state planar MSCs.

Mots clés : laser induced graphene, nanodiamond, stretchable silicone rubber, microsupercapacitor, electrochemistry

Participants :
- Brno University of Technology (Rép. Tchèque) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 144980.64 €
Projet LEECHSYMBIO (Horizon 2020, appel à projets 2018)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Life Sciences
Statut du projet : SIGNED

Strict blood-feeding leeches are confronted with a strong B-vitamin deficiency and thus rely on bacterial symbionts to supplement their unbalanced diet. Recent evidence showed that the Mexican leech Haementeria officinalis houses in a distinct specialised organ intracellular Providencia siddallii symbionts, which have a highly reduced genome but maintained genes required for B vitamin biosynthesis. The aim of this proposal is to establish this leech as a model system for studying the intricacies of nutritional blood-feeding symbioses. The genomes of Providencia symbionts of 4 additional Haementeria species will be sequenced and analysed to identify core-metabolic pathways and to reconstruct the evolutionary history of this symbiosis. A draft genome sequence of the Mexican leech Ha. officinalis will be determined, providing unique insights into metabolic complementarity. Comparison with the available genome of the related non-blood-feeding leech Helobdella robusta will facilitate identification of genomic determinants of the acquisition/loss of the bacteriome and the blood feeding habit. Bacteriome development and colonization will be investigated using different developmental stages and advanced microscopy techniques. Host control and nutritional role of the symbiont will be studied using isotope probing experiments and state-of-the-art chemical imaging and metabolomics methods. This project will provide comprehensive insights into a nutritional symbiosis in medically relevant blood-feeding animals, including the evolution of specialised symbiosis organs and metabolic interactions between both partners. It will bring together a postdoc with a strong background in bioinformatics and bacterial symbionts with a host lab focused on experimental and molecular analysis of microbe-host interactions. This set-up ensures both an ideal environment for the postdoc on his way to an truly independent researcher, and an efficient two-way transfer of knowledge.

Mots clés : leech symbiosis, b vitamin production, host microbe interactions, bacteriome development, blood, feeding parasite

Participants :
- University of Vienna (Autriche) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 186167.04 €
Projet ML4SFT (Horizon 2020, appel à projets 2019)
Action : Postdoctoral Fellowships - Global Fellowships
Panel : Physics
Statut du projet : SIGNED

String theory is one of the leading endeavours in theoretical physics to construct a theory of quantum gravity unified with all interactions and matter. As such, it provides a complete description of the Universe and of its content. However, while all ingredients are present, the details for a precise contact with the Standard Model and with our Universe are missing, mostly because the number of possible realizations is huge and no selection mechanism is known. Moreover, progress is further hindered, for the description of string theory as a field theory - arguably its most fundamental formulation - is very intricate. Since the most immediate difficulties are computational - it boils down to studying statistics of geometries (the "string landscape") and to approximating functions and geometries (to build a string field theory) - machine learning seems to provide an adequate framework to address the challenges faced by string theory. The first aspect of this project is to elaborate machine learning tools for constructing the string field theory action while deepening in parallel our analytic understanding of closed string field theory. For the latter, the main objective is to include auxiliary fields and to investigate whether the action can be made cubic. The second aspect is to design machine learning algorithms to map the string landscape. This project holds the promise of important developments in our understanding of string theory and in its applications to phenomenology. It is located at the intersection of multiple disciplines - theoretical physics, mathematics (Riemann surfaces and homotopy algebras) and machine learning. The choice of institutions and supervisors reflect this interdisciplinary aspect: Prof. Zwiebach is an expert in string (field) theory and in the moduli space geometry, while Dr. Tamaazousti is a specialist of machine learning. Furthermore, both institutions are renowned in these domains.

Mots clés : string field theory, string amplitudes, string landscape, homotopy algebra, riemann surface, machine learning

Participants :
- Commissariat à l’énergie atomique et aux énergies alternatives (France) — Coordinator, beneficiary, Recherche, subvention : 275619.84 €
- Massachusetts Institute of Technology (Etats-Unis) — Partner, organization partner, Enseignement supérieur ou secondaire, subvention : 0.0 €
Projet CortIMod (Horizon 2020, appel à projets 2015)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Information Science and Engineering
Statut du projet : CLOSED

Stroke is a leading cause of adult chronic disability and has devastating socioeconomic costs. Severe hand paresis is a major contributor to chronic disability after stroke. New research opportunities offer the prospect of accelerating both the understanding of neurological disorders and the development of new therapies to improve rehabilitation by promoting neural plasticity. This project proposes an innovative multimodal platform integrating noninvasive electrophysiological recording and stimulation devices to “neuromodulate” the motor cortex. The platform will decode cortical states preceding human motor intentions to induce highly functional neural changes in the cortical circuits controlling hand muscles. I expect that the application of this new intervention will lead to unprecedented levels of motor recovery by reinforcing patients' remaining brain-muscle connections. I recently finished my Ph.D. in the emerging field of neural engineering. In it I developed technologies to study brain activities related to voluntary and pathological motor processes in neurological patients, and I designed real-time platforms integrating cortical signals with muscle electrical stimulation for neurorehabilitation. The research in this project together with the expertise of my supervisor and host constitute an unmatchable opportunity for me to develop and consolidate critical neuroscience, neural engineering, and clinical skills, which will broaden my current competencies. I will also enrich my network of collaborations and receive training on management, tutoring, teaching, proposal writing and ethics, which will allow me to become and independent and mature researcher. I expect that my research will have high impact in the EU society and I anticipate that, through this fellowship, I will promote research in neuromodulation from a multidisciplinary perspective and devise the possibilities to effectively transfer new technologies to the clinical practice.

Mots clés : neurorehabilitation, neuromodulation technologies

Participants :
- University of London (Royaume-Uni) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 195454.8 €
Projet QUELIMANE (Horizon 2020, appel à projets 2020)
Action : Postdoctoral Fellowships - European Fellowships
Statut du projet : CLOSED

Sub-Saharan Africa is the world’s fastest-urbanizing region. The movement of people from the countryside to denser cities will likely create progress toward some of the Sustainable Development Goals in the medium term, especially SDGs 11 (Sustainable Cities and Communities), 13 (Climate Action), 8 (Decent Work and Economic Growth) and 5 (Gender Equality). However, rural-urban migration can also cause disruption and conflict in the short term. Furthermore, many autocratic national governments oppose urbanization because it increases the cost and difficulty of controlling citizens through patronage and intimidation. Therefore, the responsibility often falls on city-level governments in the developing world to peacefully integrate new migrants with existing residents, and there is an urgent need for evidence on how to do so. This project will help meet this need in the context of the Mozambican city of Quelimane (pop. 350,000). Quelimane has created a program for rural migrant integration that includes job matching, training and education, financial services, and tax incentives. The government of Quelimane has invited me and my supervisor at NovaSBE, Pedro Vicente, to conduct a randomized controlled trial (RCT) to rigorously measure the causal impact the program, by selecting randomly which blocks in the city will be eligible for the program first. To measure both the direct effect of the program and its externalities, we will survey 500 long-term residents in treatment and control blocks as well as 1000 recent migrants and their still-rural connections. We plan to study not just how the program affects livelihoods, but attitudes, prejudices, political attribution, and further migration. Understanding how Quelimane can improve the urban integration of rural – and which parties have political incentives to do so – can help other local governments in Africa build institutions to meet similar challenges, a key objective of SDG 16 (Peace, Justice and Strong Institutions).

Mots clés : development, urbanization, gender, climate change, local government, randomization, africa, mozambique, public administration

Participants :
- Universidade Nova de Lisboa (Portugal) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 159815.04 €
Projet ORANG (Horizon 2020, appel à projets 2017)
Action : Postdoctoral Fellowships - European Fellowships
Panel : Environment and Geosciences
Statut du projet : CLOSED

Subduction describes the process in which one tectonic plate descends into the mantle beneath an adjoining tectonic plate as a result of convergence. They play a fundamental role in the creation and evolution of the continents, the generation of new oceanic crust and the exchange of water between the Earth, oceans and atmosphere. Subduction zones are also the principal source of volcanic and seismic hazard around the globe. Despite having been the subject of intensive study by Earth Scientists from around the world, one aspect of the subduction cycle that has received little attention is the termination phase, where, for potentially a variety of reasons, the descent of the downgoing slab ceases. Given that all subduction zones have a finite lifetime, this process is likely to have profoundly influenced the way in which our continents have evolved, but to date no credible model has been developed for what might happen to the crust and upper mantle in such circumstances. This knowledge gap means that there is a unique and timely opportunity to provide new insight into this fundamental Earth process. I aim to make a step-change in our understanding of how post-subduction continental margins evolve and how the crust and upper mantle interact by combining seismic imaging, geodynamic modelling and geological observations in North Borneo (east Malaysia). The central objective of the proposal is to deploy a dense array of seismometers both onshore and offshore in North Borneo to record local and regional earthquakes, as well as ambient seismic noise, in order to image the underlying crust and mantle at high resolution. Our new multi-disciplinary study will allow us to build a model of how the continental lithosphere responds to subduction termination. As well as filling an important gap in our understanding of the subduction cycle and the evolution of the continents, our new model can be used as a benchmark for other post-subduction settings around the world.

Mots clés : post subduction tectonics, seismic tomography, geodynamic modeling

Participants :
- University of Cambridge (Royaume-Uni) — Coordinator, beneficiary, Enseignement supérieur ou secondaire, subvention : 195454.8 €