Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Intelligent Stochastic Computation Methods for Complex Statistical Model Learning (SMARTBAYES)

Framework: FP7 — Ideas
Programme: European Research Council
Theme: European research council
Destination: Starting grants
Action type: Support for frontier research (ERC)
Panel: Mathematical foundations
Countries involved: Finland
Call:  (ERC-2009-StG, 2009)
Start date: 2009-11-01
End date: 2014-10-31
Duration (months): 60.0
Total project cost (EUR): 550000.0
EU funding (EUR): 550000.0
Participants: 1
Project status: CLOSED

Very recently, it has been claimed that the Bayesian paradigm has revolutionized statistical thinking in numerous fields of research, as a considerable amount of novel Bayesian statistical models and estimation algorithms have gained popularity among scientists. Despite of the evident success of the Bayesian approach, there are also many research problems where the computational challenges have so far proven to be too exhaustive to promote wide-spread use of the state-of-the-art Bayesian methodology. In particular, due to significant advances in measurement technologies, e.g. in molecular biology, a constant need for analyzing and modeling very large and complex data sets has emerged on a wide scale during the past decade. Such needs are even anticipated to rapidly increase in near future with the current technological advances. The prevailing situation is therefore somewhat paradoxical, as the theoretical superiority of the Bayesian paradigm as an uncertainty handling framework is widely acknowledged, yet it can be unable to provide practically applicable solutions to complex scientific problems. To resolve this issue, the research project will have a focus on stochastic computational and modeling strategies to develop methods that overcome problems associated with the analysis of highly complex data sets. With these methods we aim to be able to solve a multitude of statistical learning problems for data sets which cannot yet be reliably handled in practice by any of the existing Bayesian tools. Our approaches will build upon recent advances in Bayesian predictive modeling and adaptive stochastic Monte Carlo computation, to create a novel family of parallel interacting learning algorithms. Several significant statistical modeling problems will be considered to demonstrate the potential of the developed methods. Our goal is also to provide implementations of some of the algorithms as freely available software packages to benefit concretely the scientific community.

Keywords: bayesian statistics,monte carlo methods,statistical model learning
Surfaces, 3-manifolds and automorphism groups (2-3-AUT)

Framework: FP7 — Ideas
Programme: European Research Council
Theme: European research council
Destination: Starting grants
Action type: Support for frontier research (ERC)
Panel: Mathematical foundations
Countries involved: Denmark
Call:  (ERC-2009-StG, 2009)
Start date: 2009-11-01
End date: 2014-10-31
Duration (months): 60.0
Total project cost (EUR): 724992.0
EU funding (EUR): 724992.0
Participants: 1
Project status: CLOSED

The scientific goal of the proposal is to answer central questions related to diffeomorphism groups of manifolds of dimension 2 and 3, and to their deformation invariant analogs, the mapping class groups. While the classification of surfaces has been known for more than a century, their automorphism groups have yet to be fully understood. Even less is known about diffeomorphisms of 3-manifolds despite much interest, and the objects here have only been classified recently, by the breakthrough work of Perelman on the Poincar\&apos;e and geometrization conjectures. In dimension 2, I will focus on the relationship between mapping class groups and topological conformal field theories, with applications to Hochschild homology. In dimension 3, I propose to compute the stable homology of classifying spaces of diffeomorphism groups and mapping class groups, as well as study the homotopy type of the space of diffeomorphisms. I propose moreover to establish homological stability theorems in the wider context of automorphism groups and more general families of groups. The project combines breakthrough methods from homotopy theory with methods from differential and geometric topology. The research team will consist of 3 PhD students, and 4 postdocs, which I will lead.

Keywords: mapping class groups of 2- and 3-dimensional manifolds,topological conformal field theory,homological stability,hochschild homology
Quantum Optomechanics: quantum foundations and quantum information on the micro- and nanoscale (QOM)

Framework: FP7 — Ideas
Programme: European Research Council
Theme: European research council
Destination: Starting grants
Action type: Support for frontier research (ERC)
Panel: Fundamental constituents of matter
Countries involved: Austria
Call:  (ERC-2009-StG, 2009)
Start date: 2009-11-01
End date: 2014-10-31
Duration (months): 60.0
Total project cost (EUR): 1670904.0
EU funding (EUR): 1670904.0
Participants: 1
Project status: CLOSED

Quantum states of mechanical resonators promise access to completely new experimental regimes of physics: from unprecedented levels of force sensitivity to the generation of macroscopic quantum superpositions of massive objects containing up to 10^20 atoms. This opens up not only exciting possibilities for novel applications but also allows to (re)address fundamental questions of quantum physics, in particular its relation to the classical world. For this reason the preparation and control of mechanical quantum states has long been an enticing but far fetched goal of breakthrough character. With the advent of micro- and nano-mechanics this goal is at the verge of becoming an experimental reality. The last few years have witnessed unprecedented global progress in pushing mechanical systems towards the quantum regime. A thriving interdisciplinary field has emerged that aims to exploit the tremendous potential that lies in the control of mechanical quantum states. The main idea of this proposal is to combine the tools and concepts of quantum optics with micro- and nano-mechanical systems. Such combination provides a unique and powerful approach that allows, with a minimal set of experimental interactions, universal quantum control over mechanical systems via opto-mechanical interactions. The feasibility of the approach has recently been verified by us and by several other groups worldwide in a series of experimental demonstrations of mechanical laser cooling. The main objective of the proposed research is to go significantly beyond the current state-of-the-art and to develop the field of quantum-opto-mechanics to its full extent, both in experiment and theory. This will also increase the European visibility in this highly topical area of research. My professional background in both solid-state physics and quantum optics and quantum information will be of additional help in this highly interdisciplinary endeavour.

Keywords: quantum information,foundations of quantum physics,nanomechanics,quantum-opto-mechanics,decoherence
Conformal coating of nanoporous materials (COCOON)

Framework: FP7 — Ideas
Programme: European Research Council
Theme: European research council
Destination: Starting grants
Action type: Support for frontier research (ERC)
Panel: Products and process engineering
Countries involved: Belgium
Call:  (ERC-2009-StG, 2009)
Start date: 2010-01-01
End date: 2014-12-31
Duration (months): 60.0
Total project cost (EUR): 1432799.99
EU funding (EUR): 1432799.99
Participants: 1
Project status: CLOSED

CONTEXT - Nanoporous structures are used for application in catalysis, molecular separation, fuel cells, dye sensitized solar cells etc. Given the near molecular size of the porous network, it is extremely challenging to modify the interior surface of the pores after the nanoporous material has been synthesized. THIS PROPOSAL - Atomic Layer Deposition (ALD) is envisioned as a novel technique for creating catalytically active sites and for controlling the pore size distribution in nanoporous materials. ALD is a self-limited growth method that is characterized by alternating exposure of the growing film to precursor vapours, resulting in the sequential deposition of (sub)monolayers. It provides atomic level control of thickness and composition, and is currently used in micro-electronics to grow films into structures with aspect ratios of up to 100 / 1. We aim to make the fundamental breakthroughs necessary to enable atomic layer deposition to engineer the composition, size and shape of the interior surface of nanoporous materials with aspect ratios in excess of 10,000 / 1. POTENTIAL IMPACT Achieving these objectives will enable atomic level engineering of the interior surface of any porous material. We plan to focus on three specific applications where our results will have both medium and long term impacts: - Engineering the composition of pore walls using ALD, e.g. to create catalytic sites (e.g. Al for acid sites, Ti for redox sites, or Pt, Pd or Ni) - chemical functionalization of the pore walls with atomic level control can result in breakthrough applications in the fields of catalysis and sensors. - Atomic level control of the size of nanopores through ALD controlling the pore size distribution of molecular sieves can potentially lead to breakthrough applications in molecular separation and filtration. - Nanocasting replication of a mesoporous template by means of ALD can result in the mass-scale production of nanotubes.

Keywords: atomic layer deposition
DYNAMIC COVALENT CAPTURE: Dynamic Chemistry for Biomolecular Recognition and Catalysis (DYCOCA)

Framework: FP7 — Ideas
Programme: European Research Council
Theme: European research council
Destination: Starting grants
Action type: Support for frontier research (ERC)
Panel: Materials and Synthesis
Countries involved: Italy
Call:  (ERC-2009-StG, 2009)
Start date: 2009-10-01
End date: 2014-09-30
Duration (months): 60.0
Total project cost (EUR): 1400000.0
EU funding (EUR): 1400000.0
Participants: 1
Project status: CLOSED

Molecular recognition plays a fundamental role in nearly all chemical and biological processes. The objective of this research project is to develop new methodology for studying and utilizing the noncovalent recognition between two molecular entities, focussing on biomolecular receptors and catalysts. A dynamic covalent capture strategy is proposed, characterized by the following strongholds. The target itself self-selects the best component out of a combinatorial library. The approach has a very high sensitivity, because molecular recognition occurs intramolecularly, and is very flexible, which allows for an easy implementation in very diverse research areas simply by changing the target. The dynamic covalent capture strategy is strongly embedded in the fields of supramolecular chemistry and (physical) organic chemistry. Nonetheless, the different work programmes strongly rely on the input from other areas, such as combinatorial chemistry, bioorganic chemistry, catalysis and computational chemistry, which renders the project highly interdisciplinary. Identified targets are new synthetic catalysts for the selective cleavage of biologically relevant compounds (D-Ala-D-Lac, cocaine and acetylcholine, and in a later stage peptides and DNA/RNA). Applicative work programmes are dedicated to the dynamic imprinting of monolayers on nanoparticles for multivalent recognition and cleavage of biologically relevant targets in vivo and to the development of new screening methodology for measuring chemical equilibria and, specifically, for the discovery of new HIV-1 fusion inhibitors.

Keywords: dynamic covalent chemistry dynamic combinatorial chemistry molecular recognition bioorganic catalysis methodology
Multifunctional Magnetic Nanoparticles: Towards Smart Drugs Design. (NANOPUZZLE)

Framework: FP7 — Ideas
Programme: European Research Council
Theme: European research council
Destination: Starting grants
Action type: Support for frontier research (ERC)
Panel: Materials and Synthesis
Countries involved: Spain
Call:  (ERC-2009-StG, 2009)
Start date: 2010-02-01
End date: 2015-12-31
Duration (months): 71.0
Total project cost (EUR): 1541310.0
EU funding (EUR): 1541310.0
Participants: 1
Project status: CLOSED

Nature has been utilizing nanostructures for billion of years. The following two properties, (i) being about the size of typical biological objects and (ii) the possibility of tailoring their properties by changing their size, make nanoparticles attractive for biomedical applications. Using nanoparticles to deliver drugs to tumours offers an attractive possibility to avoid obstacles that occur during conventional systemic drug administration. This NANOPUZZLE project pretends to develop an innovative controlled release methodology, based on hyperthermia and magnetic nanoparticles, as platform for the incorporation of different molecules with different functionalities, to obtain a multifunctional system for cancer treatment and diagnose that leads antitumoral drugs discharge only in the tumoral area. Multifunctional magnetic nanoparticles loaded with a targeting agent (folic acid) and a potent antitumoral drug (doxorubicin) will be prepared. These active molecules will be coupled to the magnetic nanoparticles (MNPs) due to complementary oligonucleotides strands (oligo-zipper). Due to the magnetic properties of these nanomaterials, a local heating induced by an alternating magnetic field, will release the drug in the desired target as a consequence of the DNA denaturation (oligo-unzipping). For this approach, the increase of temperature is only required directly in the nanoparticles and the heating of the surroundings is not needed. For instance, less quantity of nanoparticles and a weaker external magnetic field will be required, avoiding the main inconveniences of conventional hyperthermia treatments. Furthermore, the superparamagnetic properties of these MNPs will also allow their use as contrast agents for tracking and diagnosis by magnetic resonance imaging (MRI).

Keywords: magnetic nanoparticles,functionalization,targeting drug delivery,hyperthermia,biomarkers,multifunctional,oligonucleotids
The Parameterized Complexity of Reasoning Problems (COMPLEX REASON)

Framework: FP7 — Ideas
Programme: European Research Council
Theme: European research council
Destination: Starting grants
Action type: Support for frontier research (ERC)
Panel: Computer science and informatics
Countries involved: Austria
Call:  (ERC-2009-StG, 2009)
Start date: 2010-01-01
End date: 2014-12-31
Duration (months): 60.0
Total project cost (EUR): 1421130.0
EU funding (EUR): 1421130.0
Participants: 1
Project status: CLOSED

Reasoning, to derive conclusions from facts, is a fundamental task in Artificial Intelligence, arising in a wide range of applications from Robotics to Expert Systems. The aim of this project is to devise new efficient algorithms for real-world reasoning problems and to get new insights into the question of what makes a reasoning problem hard, and what makes it easy. As key to novel and groundbreaking results we propose to study reasoning problems within the framework of Parameterized Complexity, a new and rapidly emerging field of Algorithms and Complexity. Parameterized Complexity takes structural aspects of problem instances into account which are most significant for empirically observed problem-hardness. Most of the considered reasoning problems are intractable in general, but the real-world context of their origin provides structural information that can be made accessible to algorithms in form of parameters. This makes Parameterized Complexity an ideal setting for the analysis and efficient solution of these problems. A systematic study of the Parameterized Complexity of reasoning problems that covers theoretical and empirical aspects is so far outstanding. This proposal sets out to do exactly this and has therefore a great potential for groundbreaking new results. The proposed research aims at a significant impact on the research culture by setting the grounds for a closer cooperation between theorists and practitioners.

Keywords: algorithms and complexity
Sparse Structured Methods for Machine Learning (SIERRA)

Framework: FP7 — Ideas
Programme: European Research Council
Theme: European research council
Destination: Starting grants
Action type: Support for frontier research (ERC)
Panel: Computer science and informatics
Countries involved: France
Call:  (ERC-2009-StG, 2009)
Start date: 2009-12-01
End date: 2014-11-30
Duration (months): 60.0
Total project cost (EUR): 1468248.0
EU funding (EUR): 1468248.0
Participants: 1
Project status: CLOSED

Machine learning is now a core part of many research domains, where the abundance of data has forced researchers to rely on automated information processing. In practice, today, machine learning techniques are applied in two stages: practitioners first build a large set of features; then, off-the-shelf algorithms are used to solve the appropriate prediction tasks, such as classification or regression. While this has led to significant advances in many domains, I believe that the potential of machine learning is far from being fulfilled. The tenet of this proposal is that to achieve the expected breakthroughs, this two-stage paradigm should be replaced by an integrated process where the specific structure of a problem is taken into account explicitly in the learning process. This will allow the consideration of massive numbers of features, in both numerically efficient and theoretically well-understood ways. I plan to attack this problem through the tools of regularization by sparsity-inducing norms. The scientific objective is thus to marry structure with sparsity: this is particularly challenging because structure may occur in various ways (discrete, continuous or mixed) and my targeted applications in computer vision and audio processing lead to large-scale convex optimization problems. My research program is expected to have a high impact on statistical machine learning research, notably by providing new solutions to the open problem of non-linear variable selection. Moreover, my general methodology will be directly applied to domains where the natural structure of data has been recognized as crucial but is still underused by learning techniques, namely computer vision (object recognition, image denoising) and audio processing (speech separation, music recognition).

Keywords: machine learning,computer vision
Ultrahigh-Intensity Plasma Optics (PLASMOPT)

Framework: FP7 — Ideas
Programme: European Research Council
Theme: European research council
Destination: Starting grants
Action type: Support for frontier research (ERC)
Panel: Fundamental constituents of matter
Countries involved: France
Call:  (ERC-2009-StG, 2009)
Start date: 2010-01-01
End date: 2014-12-31
Duration (months): 60.0
Total project cost (EUR): 1140000.0
EU funding (EUR): 1140000.0
Participants: 1
Project status: CLOSED

Ultraintense and ultrashort light pulses have a huge potential for applications, such as the production of very compact particle accelerators. Exploiting this potential requires pushing the characteristics of lasers beyond their present state-of-the-art performances. However, the laser technology used so far is approaching its limits, in particular because of the optical breakdown of conventional optical media. Overcoming these limits requires finding radically new approaches for optics at ultrahigh laser intensities. The idea of this proposal consists in developing optical elements based on plasmas, i.e. plasma optics . Since plasmas are already ionized, they can sustain electromagnetic fields of extremely large amplitude. They can thus be exploited to produce several key optical elements needed to manipulate e.g. shorten, convert in frequency, or even amplify- existing ultraintense lasers. To this end, two main physical processes are exploited: laser-excited Langmuir waves, and the Doppler effect associated to the relativistic motion of plasmas in ultraintense laser fields. This project would contribute to the conception of a system consisting in a chain of several plasma optics, placed at the output of a table-top laser, which would deliver few-optical-cycle long PetaWatt-class near-visible light pulses, as well as Terawatt-class attosecond pulses in the soft x-ray range. Such light sources would open exciting perspectives in Science and Technology. More fundamentally, this project will exploit the coherent light emission induced during relativistic laser-plasma interaction as a fine probe of the ultrafast plasma dynamic. This new type of diagnostic should lead to significant progresses in the understanding of laser-plasma interaction at extreme laser intensities.

Keywords: ultrahigh and ultrashort laser pulses,relativistic dynamic of plasmas
Understanding the Physics of Galaxy Formation and Evolution at High Redshift (UPGAL)

Framework: FP7 — Ideas
Programme: European Research Council
Theme: European research council
Destination: Starting grants
Action type: Support for frontier research (ERC)
Panel: Universe sciences
Countries involved: France
Call:  (ERC-2009-StG, 2009)
Start date: 2009-11-01
End date: 2014-10-31
Duration (months): 60.0
Total project cost (EUR): 939600.0
EU funding (EUR): 939600.0
Participants: 1
Project status: CLOSED

Understanding the processes regulating galaxy formation is a major open issue in observational cosmology. We now have a fairly detailed census of the diverse high-z galaxy populations, hence time is ripe for fundamental advances in understanding galaxy formation and evolution in the crucial first few billion years. This requires to observationally constrain and clarify the physical processes that operated at those early epochs. Thanks to a new galaxy selection technique that I recently introduced, I have been leading research projects that have now provided major new results on high redshift z~2 galaxies. These include molecular gas first seen in typical high-z galaxies; the major phase of star formation at very high rates; widespread presence of previously unknown Compton-thick AGNs inside massive galaxies; and the existence of evolved galaxy clusters containing X-ray emitting gas already at z~2. Building on the legacy of these discoveries and critical results, I ask for support to fund the establishment of a new research team to lead research aimed at exploring the physics of galaxy formation in the distant Universe. With three postdocs each year for a total of 5 years, we will pave new avenues towards understanding the relation between black holes and galaxies at the time of their major mass growth and assembly. In a full multiwavelength approach, by obtaining and using data from all major observational facilities (both in space and on the ground) we will aim to clarify the physical trigger of downsizing, catch AGN feedback in action and assess its role in galaxy transformations, along with the effects of the environment, gas accretion, star formation and merging in driving galaxy formation.

Keywords: high redshift galaxies
Electronic devices based on nanolayers (FLATRONICS)

Framework: FP7 — Ideas
Programme: European Research Council
Theme: European research council
Destination: Starting grants
Action type: Support for frontier research (ERC)
Panel: Condensed matter physics
Countries involved: Switzerland
Call:  (ERC-2009-StG, 2009)
Start date: 2009-09-01
End date: 2014-08-31
Duration (months): 60.0
Total project cost (EUR): 1799996.0
EU funding (EUR): 1799996.0
Participants: 1
Project status: CLOSED

The main objective of this research proposal is to explore the electrical properties of nanoscale devices and circuits based on nanolayers. Nanolayers cover a wide span of possible electronic properties, ranging from semiconducting to superconducting. The possibility to form electrical circuits by varying their geometry offers rich research and practical opportunities. Together with graphene, nanolayers could form the material library for future nanoelectronics where different materials could be mixed and matched to different functionalities.

Keywords: nanoelectronics,nanodevices,nanolayers,transistors