Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : SHAPE (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Products and processes engineering
Entité participante : Politecnico di Milano — Pays : Italie — Rôle : PI
Acronyme de l'entité : POLIMI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1496250.0 €

Despite the fact that the catalyst structure has been an important factor in catalysis science since the discovery of structure sensitive reactions in single crystal studies, its effect on reactivity is neglected in state-of-the-art microkinetic modelling. In reality, the catalyst is dynamic by changing its structure, shape and size in response to the different conditions in the reactor. Thus, the inclusion of such effects within the framework of microkinetic modelling, albeit extremely complex, is of outmost importance in the quest of engineering the chemical transformation at the molecular level. This proposal aims to approach this grand challenge by developing a hierarchical multiscale methodology for the structure-dependent microkinetic modelling of catalytic processes in applied catalysis. In particular this challenging objective will be achieved by acting on two main fronts: i. development of a hierarchical multiscale methodology for the prediction of the structural changes of the catalyst material as a function of the operating conditions in the reactor and the analysis of the structure-activity relations through the development of structure-dependent microkinetic models; ii. show the applicability of the methodology by the assessment of the structure-activity relation in the context of relevant processes in energy applications such as the short-contact-time CH4 reforming with H2O and CO2 on supported-metal catalysts. The inherent complexity of the problem will be tackled by hierarchically combining novel methods at different levels of accuracy in a dual feed-back loop between theory and experiments. This will require interdisciplinary efforts in bridging among surface science, physical-chemistry and chemical engineering. The fundamental nature and impact of the methodology will be unprecedented and will pave the way toward the detailed analysis and design of the structure-activity relation by tuning shape and size to tailoring activity and selectivity.

Mots-clés : heterogeneous catalysis,microkinetic modeling,multiscale modeling,chemical kinetics,methane reforming,metal catalysts,structure sensitivity
Projet ERC : NatDyReL (Horizon 2020, appel 2018, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : TU Wien — Pays : Autriche — Rôle : PI
Acronyme de l'entité : TUW
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 413797.46 €

Despite the significant progress made in the field of humanoid robotics over the last 10-15 years, bipedal locomotion in robotics is still far from human performance in terms of speed, versatility, and robustness. The design of most humanoid robots nowadays is dominated by the aim at high rigidity and position accuracy in the motor units. In contrast, the NatDyReL project aims at a fundamental shift of paradigm in the design and control of humanoid robots, towards a new generation of intrinsically compliant robots that can adjust their open loop actuator impedance in real-time to the task. We believe that the maturing technology of variable impedance actuators in combination with novel control approaches for the intrinsically elastic dynamics has the potential of bringing humanoid locomotion and multi-contact motions to a new level in terms of energy-efficiency and execution speeds more similar to the human archetype. However, to fully utilize the ultimate benefits promised by variable impedance actuators, i.e. to store and release energy as well as to provide physical protection against shocks caused by impacts, it is necessary to exploit the natural compliant whole body dynamics on all levels of the system design, planning and control hierarchies. This project follows two scientific tracks for achieving (a) energetically efficient and high performant legged locomotion and (b) robust and dynamic contact transitions and in-contact motions for whole body locomotion in uncertain and confined spaces. As a strong basis to the mentioned application oriented objectives, we also aim at fundamental contributions on the control challenges related to novel variable impedance actuator technologies. The project is expected to make a strong impact on bipedal humanoid locomotion. Moreover, the developed methods will be sufficiently general such that they can also be transferred to other morphologies such as e.g. multi-limbed walking or climbing robots.

Mots-clés : humanoid robots,locomotion,walking machines,natural dynamics
Projet ERC : COSMIC-DANCE (Horizon 2020, appel 2015, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : Université de Bordeaux — Pays : France — Rôle : PI
Acronyme de l'entité : UBx
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1859413.0 €

Despite the tremendous progress achieved over the past decade, the study of stellar formation is far from complete. We have not yet measured the minimum mass for star formation, nor the shape of the IMF down to the least massive free-floating planets, or know how universal this shape is. Although clusters are the building blocks of galaxies, little is known about their early dynamical evolution and dispersal into the field. The main culprit for this state of affairs is the high level of contamination and incompleteness in the sub-stellar regime, even for the best photometric and astrometric surveys. COSMIC-DANCE aims at overcoming these drawbacks and revealing the shape of the IMF with a precision and completeness surpassing current and foreseeable surveys of the next 15 years. We will: 1) Measure: using a groundbreaking, proven and so far unique method I designed, we will measure proper motions with an accuracy comparable to Gaia but 5 magnitudes deeper, reaching the planetary mass domain, and, critically, piercing through the dust obscured young clusters inaccessible to Gaia’s optical sensors. 2) Discover: feeding these proper motions and the multi-wavelength photometry to innovative hyper-dimensional data mining techniques, we will securely identify cluster members within the millions of sources of the COSMIC-DANCE database, complemented by Gaia at the bright end, to obtain the final census over the entire mass spectrum for 20 young nearby clusters, the end of a 60-year quest. 3) Understand: by providing conclusive empirical constraints over a broad parameter space unaccessible to current state-of-the-art surveys on the much debated respective contributions of evolutionary effects (dynamics, feedback and competitive accretion) and initial conditions (core properties) to the shape and bottom of the IMF, the most fundamental and informative product of star formation, with essential bearings on many areas of general astrophysics.

Mots-clés : initial mass function,astrometry,proper motion,brown dwarf,planetary mass object
Projet ERC : INTEGRATE (Horizon 2020, appel 2019, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Earth system science
Entité participante : Stockholm University — Pays : Suède — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2748920.0 €

Despite their abundance in the Earth’s atmosphere, cloud formation and evolution are still poorly understood. This is particularly true for the interactions clouds have with atmospheric aerosol particles and their precursor vapors, which hampers our knowledge on the role that clouds and precipitation play in the climate system and in governing air quality. I argue that a major fraction of these uncertainties stem from inconsistencies and discontinuities in treating 1) the molecular phase transitions driving the dynamic cloud formation processes; 2) the interplay between atmospheric chemical composition and atmospheric dynamics; 3) the complex role that clouds play as both sources and sinks of particulate matter, but also as subjects to changes driven by aerosol particles. INTEGRATE will address these inconsistencies, and acquire a comprehensive picture of the phase transitions leading to cloud formation over the relevant scales. INTEGRATE will fill the key knowledge gaps through work in 1) the process scale, developing, applying and evaluating descriptions of molecular phase transitions within clouds; 2) the cloud scale, integrating the relevant chemical, microphysical and dynamic phenomena in a 3-dimensional cloud simulation framework; 3) the regional and global scales, studying interactions between aerosol loadings and clouds in past, present and future climates using chemical transport and earth system modeling. Systematic approaches for bridging the gap between the various time and spatial scales will also be developed within INTEGRATE. If successful, INTEGRATE will open new avenues for 1) fundamental understanding of the physics and chemistry of atmospheric phase transitions; 2) improved climate projections and ultimately better policies for reaching the targets of the Paris agreement; 3) better predictions of factors controlling air quality, hence facilitating the design of better policies to improve the quality of the air we breathe.
Projet ERC : SIMULACRON (Horizon 2020, appel 2019, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Technical University of Munich — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : TUM
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 3500000.0 €

Despite their amazing success, we believe that computer vision algorithms have only scratched the surface in terms of understanding our world from images. While most research on 3D reconstruction has been concerned with recovering the surface geometry and reflectance, SIMULACRON is focused on inferring the underlying physics (masses, elasticity, momenta, forces, etc.) and a simulation of the observed action directly from videos. This not only provides a more profound understanding of the observed phenomena, but it also allows us to interpolate and extrapolate complex actions far beyond the observation: The inferred physical simulation can be employed for space-time super-resolution and for predictions into the future. SIMULACRON covers three lines of research: A) We will develop algorithms for deformable shape modeling. We will explore suitable representations of 3D shape and its evolution that enable the efficient computation of shape deformation, correspondence, interpolation and extrapolation. These techniques will form the basis for inferring physical simulations in parts B and C. B) We will develop variational methods for inferring physical simulations from videos. We will compute a reference shape and simulation parameters that generate the shape deformation that is most consistent with the observations. C) We will develop learning-based approaches for inferring physical simulations from videos. We will pursue two alternative approaches: First, we will generate synthetic training data by simulating deformable shapes and the associated camera observations. Second, we will devise self-supervised techniques for learning from real data without requiring labeled training data. By shifting from inference of 3D geometry to inference of physical simulations, SIMULACRON will give rise to a more profound notion of dynamic scene understanding in computer vision, robotics and beyond. We believe that we have the necessary competence to pursue this project.

Mots-clés : 3d computer vision,multiple view reconstruction,dynamic scene understanding,physics based modeling
Projet ERC : 3D Reloaded (Horizon 2020, appel 2014, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Technical University of Munich — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : TUM
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2000000.0 €

Despite their amazing success, we believe that computer vision algorithms have only scratched the surface of what can be done in terms of modeling and understanding our world from images. We believe that novel image analysis techniques will be a major enabler and driving force behind next-generation technologies, enhancing everyday life and opening up radically new possibilities. And we believe that the key to achieving this is to develop algorithms for reconstructing and analyzing the 3D structure of our world. In this project, we will focus on three lines of research: A) We will develop algorithms for 3D reconstruction from standard color cameras and from RGB-D cameras. In particular, we will promote real-time-capable direct and dense methods. In contrast to the classical two-stage approach of sparse feature-point based motion estimation and subsequent dense reconstruction, these methods optimally exploit all color information to jointly estimate dense geometry and camera motion. B) We will develop algorithms for 3D shape analysis, including rigid and non-rigid matching, decomposition and interpretation of 3D shapes. We will focus on algorithms which are optimal or near-optimal. One of the major computational challenges lies in generalizing existing 2D shape analysis techniques to shapes in 3D and 4D (temporal evolutions of 3D shape). C) We will develop shape priors for 3D reconstruction. These can be learned from sample shapes or acquired during the reconstruction process. For example, when reconstructing a larger office algorithms may exploit the geometric self-similarity of the scene, storing a model of a chair and its multiple instances only once rather than multiple times. Advancing the state of the art in geometric reconstruction and geometric analysis will have a profound impact well beyond computer vision. We strongly believe that we have the necessary competence to pursue this project. Preliminary results have been well received by the community.

Mots-clés : 3d reconstruction,shape analysis,optimization
Projet ERC : BRISC (Horizon 2020, appel 2015, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : The Human Mind and Its Complexity
Entité participante : Ludwig-Maximilians-Universität München — Pays : Allemagne — Rôle : Partner
Acronyme de l'entité : LMU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 0.0 €

Despite their many successes and great computational power and speed, why are machines still so blatantly outperformed by humans in uncertain environments that require flexible sensorimotor behavior like playing football or navigating a disaster zone? Answering this question requires understanding the mathematical principles of biological sensorimotor control and learning. Over the recent years Bayes-optimal actor models have widely become the gold standard in the mathematical understanding of sensorimotor processing in well-controlled laboratory tasks. However, these models quickly become intractable for real-world problems because they ignore the computational effort required to search for the Bayes-optimum. What is therefore needed is a framework of sensorimotor processing that takes the limited information-processing capacity of bounded rational actors into account and that explains their robust real-world performance. It is the aim of BRISC to establish such a framework by drawing out theoretical predictions and gathering experimental evidence in human motor control, in particular to understand (i) how single bounded rational actors deviate from Bayes-optimal behavior in motor tasks, (ii) how multiple bounded rational actors organize themselves to solve motor tasks that no individual can solve by themselves and (iii) how this drives the emergence of hierarchical control structures that simultaneously process multiple degrees of abstraction at different time scales. Understanding how abstract concepts are formed autonomously from the sensorimotor stream based on resource allocation principles will establish an essential missing link between high-level symbolic and low-level perceptual processing. These advances will provide a decisive step towards a framework for robust and flexible sensorimotor processing, which is not only essential for understanding the fundamental principles of intelligent behavior, but it is also of potentially great technological value.

Mots-clés : motor control,decision making,sensorimotor learning,biological information processing,computational modeling,cognitive modeling
Projet ERC : DarkQuest (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : Max Planck Society — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : MPG
Type d'entité : Recherche
Financement ERC de l'entité : 2000000.0 €

Despite two decades of intensive efforts after the discovery of the accelerating expansion of the Universe, the nature of dark energy that dominates 68 percent of the energy density remains unknown. The majority of the remainder is in dark matter. Similarly, the elementary particles which constitute its mass is unidentified. Galaxy clusters trace the highest peaks in the cosmic density field and offer an independent and powerful probe of the growth of structure. Their overall abundance on the sky is strongly dependent on underlying cosmology. With the new availability of all sky surveys of galaxy clusters in the X-ray band with eROSITA and the utility of weak lensing signal in accurate estimation of cluster masses, we are on the verge of constraining cosmology with percent level precision. We propose to measure the energy density of the total matter, the normalization of the power spectrum of dark energy, the dark energy equation-of-state, and neutrino masses with a percent level accuracy. All sky survey observation with eROSITA and complimentary weak lensing observations with HSC also offer new prospects to test general relativity at large scales, and constrain well-motivated dark matter models (e.g. warm dark matter and self-interacting dark matter). Europe will have an opportunity to be on the forefront of observational cosmology and set the stage for the upcoming experiments if this proposal is funded. Given my expertise in the field of galaxy clusters and my role as the chair of the clusters and cosmology working group of the eROSITA consortium, I am uniquely suited to lead this effort and accomplish the goals of this proposal.

Mots-clés : all sky surveys,weak lensing,x rays,clusters of galaxies,dark matters,dark energy,cosmology
Projet ERC : POLYMMUNE (Horizon 2020, appel 2018, Proof of concept grants)
Entité participante : Centro de Investigacion Principe Felipe — Pays : Espagne — Rôle : PI
Acronyme de l'entité : CIPF
Type d'entité : Recherche
Financement ERC de l'entité : 149938.0 €

Despite various existing drug delivery methods, the key challenge remains: how to deliver drugs to the desired site of therapeutic action to achieve best treatment outcome, while minimising side effects? This is even more challenging in cancer immunotherapy as a complex interplay of immune reactions need to be activated. POLYMMUNE will evaluate the technical and commercial viability of our novel polymeric nanocarrier platform (developed during my ERC Consolidator grant MyNano) for targeted drug delivery of a wide variety of diseases and therapeutics. As proof of concept, we will focus on immunotherapy delivery for metastatic melanoma. Globally ~132,000 new melanoma cases will be diagnosed each year and despite recent successes, only 50% of the patients respond to novel immunotherapies that are costly, while causing severe side effects. Coupling a commercially available antigen against metastatic melanoma to our nanocarrier will activate a broad immune response, resulting in an effective cancer vaccine that potentially targets different melanoma types, enabling off-the-shelf production. This dual action will lead to increased clinical benefit, which will come with less side effects and will be 5-10-fold cheaper than current treatments. Additionally, our conjugate is industrially scalable and thus overcome a major bottle neck of current nanocarrier-based medicine. We envision our platform technology to be used as a method of choice for drug delivery in many medical applications, such as cancer immunotherapy and neurodegenerative diseases. These applications are within reach, as depending on the modification, our conjugates can e.g. bypass the blood brain barrier and potentially be administered intranasally (nanogel). Our platform offers a highly attractive business case, as biotechnology and pharmaceutical companies heavily invest in nanoconjugates due to the need for novel drug delivery strategies.

Mots-clés : polypeptide based carrier,polymer therapeutics,immunotherapy,advanced drug delivery,advanced melanoma
Projet ERC : IMAGINE (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Condensed matter physics
Entité participante : Centre national de la recherche scientifique — Pays : France — Rôle : PI
Acronyme de l'entité : CNRS
Type d'entité : Recherche
Financement ERC de l'entité : 1498810.0 €

Detecting and imaging magnetic fields with high sensitivity and nanoscale resolution is a topic of crucial importance for a wealth of research domains, from material science, to mesoscopic physics, and life sciences. This is obviously also a key requirement for fundamental studies in nanomagnetism and the design of innovative magnetic materials with tailored properties for applications in spintronics. Although a remarkable number of magnetic microscopy techniques have been developed over the last decades, imaging magnetism at the nanoscale remains a challenging task. It was recently realized that the experimental methods allowing for the detection of single spins in the solid-state, which were initially developed for quantum information science, open new avenues for high sensitivity magnetometry. In that spirit, it was recently proposed to use the electronic spin of a single nitrogen-vacancy (NV) defect in diamond as a nanoscale quantum sensor for scanning probe magnetometry. This approach promises significant advances in magnetic imaging since it provides quantitative and vectorial magnetic field measurements, with an unprecedented combination of spatial resolution and magnetic sensitivity, even under ambient conditions. The IMAGINE project intend to exploit the unique performances of scanning-NV magnetometry to achieve major breakthroughs in nanomagnetism. We will first explore the structure of domain walls and individual skyrmions in ultrathin magnetic wires, which both promise disruptive applications in spintronics. This will lead (i) to solve an important academic debate regarding the inner structure of domain walls and (ii) to the first detection of individual skyrmions in ultrathin magnetic wire under ambient conditions. This might result in a new paradigm for spin-based applications in nanoelectronics. We will then explore orbital magnetism in graphene, which has never been observed experimentally and is the purpose of surprising theoretical predictions.

Mots-clés : single spin physics,nanomagnetism,spintronics