Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Regions and DIHs alliance for AI-driven digital transformation of European Manufacturing SMEs (AI REGIO)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Action type: Innovation actions
Countries involved: Italy,Italy,Italy,Germany,Spain,Italy,Italy,Spain,Germany,Germany,Spain,Spain,Germany,Spain,Spain,Cyprus,Finland,Slovenia,Serbia,Belgium,France,France,France,Netherlands,Netherlands,Portugal,Luxembourg,Portugal
Call: I4MS (phase 4) - uptake of digital game changers (H2020-DT-2019-2, 2019)
Start date: 2020-10-01
End date: 2023-09-30
Duration (months): 36.0
Total project cost (EUR): 9185923.36
EU funding (EUR): 7999206.99
Participants: 40
Project status: SIGNED

The AI REGIO project aims at filling 3 major gaps currently preventing AI-driven DIHs from implementing fully effective digital transformation pathways for their Manufacturing SMEs: at policy level the Regional vs. EU gap; at technological level the Digital Manufacturing vs. Innovation Collaboration Platform gap; at business level the Innovative AI (Industry 5.0) vs Industry 4.0 gap. POLICY. Regional smart specialization strategies for Efficient Sustainable Manufacturing and Digital Transformation (VANGUARD initiative for Industrial Modcernisation) are so far insufficiently coordinated and integrated at cross-regional and pan-EU level. SME-driven >AI innovations cannot scale up to become pan-EU accessible in global marketplaces as well as SME-driven experiments remain trapped into a too local dimension without achieving a large scale dimension. Regional vs. EU Gap. TECHNOLOGY. Digital Manufacturing Platforms DMP and Digital Innovation Hubs DIH play a fundamental role in the implementation of the Digital Single Market and Digitsing European Industry directives to SMEs, but so far such initiatives, communities, innovation actions are running in a quite independent if not siloed way, where very often Platform-related challenges are not of interest for DIHs and Socio-Business impact not of interest for DMP. DMP vs. DIH Gap. BUSINESS. Many Industrial Data Platforms based on IOT Data in Motion and Analytics Data at Rest have been recently developed to implement effective Industry 4.0 pilots (I4MS Phase III platforms). The AI revolution and the new relationship between autonomous systems and humans (Industry 5.0) has not been properly addressed in I4MS so far. AI I5.0 vs. I4.0 Gap. AI REGIO is following the 4 steps for VANGUARD innovation strategy (learn-connect-demonstrate-commercialize) by constantly aligning its methods with the AI DIH Network initiative and its assets with I4MS/DIH BEinCPPS Phase II and MIDIH / L4MS Phase III projects. AI REGIO: Industry 5.0 for SMEs

Keywords: ai for manufacturing smes,vanguard initiative for industrial modernisation,i4ms phases continuity,dih and regional smart specialisation strategy alignment,ai dih network initiative estension
A New Strategy for Gravity and Black Holes (GravBHs)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Fundamental constituents of matter
Panel description: Particle, nuclear, plasma, atomic, molecular, gas, and optical physics
Countries involved: Spain
Call: ERC Advanced Grant (ERC-2015-AdG, 2015)
Start date: 2016-10-01
End date: 2021-09-30
Duration (months): 60.0
Total project cost (EUR): 2138825.0
EU funding (EUR): 2138825.0
Participants: 1
Project status: CLOSED

General Relativity (GR) encompasses a huge variety of physical phenomena, from the collision of astrophysical black holes, to the dynamics (via holography) of strongly-coupled plasmas and the spontaneous symmetry-breaking in superconductors. Black holes play a central role in all this. However, their equations are exceedingly hard to solve. The apparent lack of a generic tunable parameter that allows to solve the theory perturbatively (like the electric coupling constant in electrodynamics, or the rank of the gauge group in large-N Yang-Mills theory) is arguably the single most important obstacle for generic efficient approaches to the physics of strong gravity and black holes. I argue that one natural parameter suggests itself: GR can be defined in an arbitrary number of dimensions D. Recently I have demonstrated that the limit of large D is optimally tailored for the investigation of black holes, classical and potentially also quantum. Explicit preliminary studies have proved that the concept is sound, powerful, and applicable even in four dimensions. This encourages the pursuit of a full-scale program with two major goals: (A) Reformulating GR and Black Hole physics around the large-D limit in terms of an effective membrane theory of black holes, coupled (non-perturbatively in 1/D) to an effective theory for gravitational radiation. (B) Resolution of outstanding problems in gravitational physics, in particular of problems of direct relevance to cosmic censorship (critical collapse, endpoint of black brane instabilities), and of the quantum theory of black holes. With the new tools of (A), a large number of additional problems in black hole physics and in holographic duality can be solved, which guarantee very substantial fallback objectives. These include black hole collisions, black hole phase diagrams, instabilities, holographic dynamics of finite-temperature systems, and potentially any problem that can be formulated in an arbitrary number of dimensions.

Keywords: general relativity,black holes,d expansion,effective theories,gravitational radiation,cosmic censorship
GRowing Advanced industrial Crops on marginal lands for biorEfineries (GRACE)

Framework: Horizon 2020 — Societal Challenges
Programme: Food security, sustainable agriculture and forestry, marine and maritime and inland water research and the bioeconomy
Theme: Public-private partnerships
Destination: Bio-based Industries
Action type: Innovation actions
Partnership: BBI (Art-187)
Countries involved: Italy,Italy,Italy,Italy,Italy,Austria,Germany,Italy,Italy,Italy,Germany,Croatia,Switzerland,France,France,France,Netherlands,Netherlands,Netherlands,United Kingdom,United Kingdom
Call: Improvement and adaptation of industrial crop varieties and novel sources of biomass to diversify biomass feedstock for biorefineries (H2020-BBI-JTI-2016, 2016)
Start date: 2017-06-01
End date: 2022-12-31
Duration (months): 67.0
Total project cost (EUR): 15007279.78
EU funding (EUR): 12324632.86
Participants: 29
Project status: CLOSED

In the EU-projects OPTIMSC and MultiHemp promising miscanthus and hemp germplasm was identified for crop production suitable for various end uses. In OPTIMISC also a large number of genotypes were screened for various stress tolerances (e.g. frost, drought, salinity) which are key traits for good performance under marginal conditions. However, both projects worked on small trial plots and identified utilization options only at lab scale. Miscanthus or hemp varieties that are specifically suitable for marginal lands are not yet available. A major bottleneck for development of such varieties is the lack of information on their large scale performance. Therefore the next step to develop these biomass crops for the growing bioeconomy is to demonstrate the feasibility of upscaling their production. Cultivars also need to meet the quality requirements of the specific end uses. Based on knowledge gained by the projects OPTIMISC and MultiHemp, the biomass composition of the germplasm is largely known and can be matched to the specific end uses. However, the upscaling of these value chains with tailored germplasm is not yet proven and represents a bottle neck for their wider application. The objective of this project is to demonstrate 1) the upscaling of crop production of miscanthus and hemp genotypes matched to end use and 2) their suitability for marginal, contaminated and unused land. Another aim of the project is to demonstrate the upscaling of the most promising biomass valorization chains with tailored genotypes. Various valorization options will be tested by associated partners (industry panel) and a subset will be demonstrated at (pre)commercial scale. The overall aim of the project is to have commercial cultivars, which are suitable for marginal, contaminated or unused land, available at the end of the project with proven feasibility for a set of end-uses. This includes their performance in the value chain, but also their environmental and economic profile.

Keywords: miscanthus,hemp,biomass,bioeconomy,biobased,industry,feedstock,biorefinery,crops,underutilized
Probabilistic Foundations for Networks (ProFoundNet)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Computer science and informatics
Panel description: Informatics and information systems, computer science, scientific computing, intelligent systems
Countries involved: United Kingdom
Call: ERC Starting Grant (ERC-2015-STG, 2015)
Start date: 2016-04-01
End date: 2021-03-31
Duration (months): 60.0
Total project cost (EUR): 1500000.0
EU funding (EUR): 1500000.0
Participants: 1
Project status: CLOSED

In an ever-connected world, increasingly complex network systems play a crucial role in many daily tasks. This results in an acute need for methods/tools that can enable easy control of the network and, at the same time, provide rigorous guarantees about its behavior, performance, and security. Recent years saw the growth of a new software ecosystem -Software-defined networking (SDN)- which advocates a clean and open interface between networking devices and the software that controls them. Yet, existing SDN languages do not support reasoning about crucial quantitative aspects, such as: ``How much congestion is there?'' or ``Is the network resilient under failure?''. Enabling compositional quantitative reasoning is the major breakthrough needed to fully realize the vision of SDN. The central objective of this project is to develop new abstractions for programming of networks, with high-level modular constructs. We will provide rigorous semantic probabilistic foundations, enabling quantitative reasoning. This will serve as a solid platform for program analysis tools where compositional reasoning about complex interactions will be a reality. Our goal will be achieved through an interdisciplinary research effort: using techniques from concurrency and formal methods, areas where akin challenges can be found in the quest to design correct software systems. We will leverage the wealth of recent advances in those areas (some of which from the PI's own research) to networks, and bring awareness and new challenges arising from applications in networking to the other two communities. The project will significantly advance the foundations of network programming/verification in new and previously unexplored directions. This line of research will not only result in fundamental theoretical contributions and insights in their own right but will also impact the practice of network programming and lead to new and more powerful techniques for the use of engineers and programmers.

Keywords: programming languages,software defined networks,concurrency,process algebra,coalgebra,probabilistic verification,quantitative methods
Big Data Value ecosystem (BDVe)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Action type: Coordination and support actions
Partnership: BDVA (cPPP)
Countries involved: Germany,Spain,Ireland,Germany,Spain,Spain,Germany,Belgium,Latvia,Netherlands
Call: Big data PPP: Support, industrial skills, benchmarking and evaluation (H2020-ICT-2016-1, 2016)
Start date: 2017-01-01
End date: 2020-12-31
Duration (months): 48.0
Total project cost (EUR): 4940286.25
EU funding (EUR): 4940286.25
Participants: 13
Project status: CLOSED

The mission of BDVe is to support the Big Data Value PPP in realizing a vibrant data-driven EU economy or said in other words, BDVe will support the implementation of the PPP to be a SUCCESS. Behind that mission, there are multiple goals to achieve, which should be taken into full consideration when defining the directions of the PPP. Some of the most challenging ones are: (1) achieving a more competitive landscape of European Big Data providers, leading to bigger market share; (2) creating the context for a more competitive EU industry (transport, manufacturing, public sector, agrifood, media, energy…) in the advent of a data-driven revolution where many traditional players will have to transform their processes and re-think their business if they want to remain completive –or in some cases, just to survive-; (3) ensuring the sustainability of the investments and actions triggered by the PPP. BDVe has broken down those high-level goals into 7 major priorities for the project: • Being accurately informed about most important facts in Big Data so that we have a solid basis to support the decision-making process in the PPP • Supporting the implementation of the Big Data PPP from an operational point of view • Developing a vibrant community around the PPP • Supporting the development of a European network of infrastructures and centers of excellence around Big Data • Setting-up a professional Communications strategy • Setting up a framework that supports the acceleration of data-driven businesses, and • Ensuring the sustainability of the investments and actions triggered by the PPP. The BDVe consortium includes a set of partners that have shown commitment and dedication to the success of the PPP for several years. They have already invested and they have committed to invest along the coming years. We believe that this CSA cannot be a neutral action that offers operational support without further commitment.
Bayesian Learning in the Infant Brain (BabyBayes)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Global Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Life Sciences
Countries involved: France,United States
Call: Individual Fellowships (H2020-MSCA-IF-2018, 2018)
Start date: 2019-05-01
End date: 2021-10-31
Duration (months): 30.0
Total project cost (EUR): 216303.36
EU funding (EUR): 216303.36
Participants: 2
Project status: CLOSED

Infants come into the world equipped with amazingly powerful learning competences. The past decades have witnessed an ever-growing series of discoveries about young infants’ early cognitive achievements, illustrating these fast and unique adaptive abilities. However, the brain bases of learning mechanisms in the developing brain remain poorly understood. Meanwhile, in the past decade, the Bayesian brain has been put forward as a promising computational model accounting learning processes in the mature adult brain. In the present project, I propose to bring the two communities together and inspect the validity of the influential Bayesian brain hypothesis when learning processes are most crucially shaping the brain. Two assumptions derived from the Bayesian theory will challenged with studies conducted in young infants, using non-invasive behavioral and brain imaging techniques. In a first study, I will inspect whether the infant brain actively propagates predictions about upcoming events, as hypothesized in the Bayesian framework. In a second study, I propose an integrated application of computational modeling, neural recordings and behavioral measurements to test a second assumption of the Bayesian brain which postulates that the brain continuously tracks and adjusts to the progressive discovery of regular patterns in the input. I will establish an international research network and rely on two hosts with distinct expertise to provide me with new skills. By bridging multiple levels of description, this research program will open up translational perspectives for the understanding of developmental processes and ultimately for the diagnosis of atypical development.

Keywords: brain imaging,brain development,learning,prediction,attention
environmental Conflicts through the Lens of Artwork and Multimedia in waterscape transfORrmations (CLAMOR)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Global Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Social Sciences and Humanities
Countries involved: Spain,United States
Call: Individual Fellowships (H2020-MSCA-IF-2017, 2017)
Start date: 2018-09-22
End date: 2022-05-16
Duration (months): 36.0
Total project cost (EUR): 257191.2
EU funding (EUR): 257191.2
Participants: 2
Project status: CLOSED

The fellowship aims at establishing the applicant as an expert in the study of environmental conflicts through training in the interdisciplinary fields of (gendered) water justice and data-intensive social science at the University of California Berkeley. I will analyse artworks produced in water-related ecological distribution conflicts (EDC) in which transformative or restorative initiatives operate under violent conditions. The use of artworks in EDC has not been theorised enough although they are crucial to elicit the valuation languages held by local populations, this being a main tenet of ecological economics, and more generally of the environmental social sciences. I will conduct empirical research on the gendered use in EDC of artistic creations (films, plastic arts and songs), connected multimedia, and supporting ICTs, at two levels: a) case studies drawing on activist knowledge of EDC over wetlands and over coal (both water-related) under diverse contexts of violence in Colombia and California; and b) databases on artworks, and on social media, from EDC in countries where there is violence against environmental defenders (e.g., Philippines and Brazil). The research and associated training will innovatively use mixed methods, combining in-depth case studies, ideal for developing causal explanations, with data-intensive social science research methods such as big-data extended ethnography, which provide grounds for generalisation. CLAMOR will address environmental justice issues that are of relevant for Europe and will build up a large database of artworks used in EDC at the global level. The action contributes to two H2020’s societal challenges and reinforces the European Innovation Partnership on Water, and the Water Justice Action Group in particular. Knowledge will be reintegrated at the Universitat Autònoma de Barcelona through postgraduate teaching and supervision, and by establishing a distinctive line of research on arts and environmental conflicts.

Keywords: environmental justice,ecological distribution conflicts,environmental activism,wetlands,coal,environmental conflict databases,data intensive social research,case studies,colombia,california
Bridging Scales in Random Materials (RandSCALES)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Mathematics
Panel description: All areas of mathematics, pure and applied, plus mathematical foundations of computer science, mathematical physics and statistics
Countries involved: Austria
Call: ERC STARTING GRANTS (ERC-2020-STG, 2020)
Start date: 2021-03-01
End date: 2026-02-28
Duration (months): 60.0
Total project cost (EUR): 1142899.0
EU funding (EUR): 1142899.0
Participants: 1
Project status: SIGNED

The identification and justification of scaling limits is a central theme in modern PDE theory, reflected for instance in the theories of homogenization and singular limits. In many multiscale PDE models, randomness plays a crucial role: In random media, the quantitative homogenization process is driven by decorrelation and concentration of measure; for ill-posed evolution problems like many interface evolution equations, random noise may provide a regularization, potentially restoring well-posedness and hence approximability by numerical schemes. In the present project, we pursue a program to achieve a deeper understanding of the role of randomness in multiscale PDEs. We focus on three important, yet largely unexplored, aspects: A) We develop a quantitative stochastic homogenization theory for nonlinear material models, ranging from variational models for brittle fracture over models from statistical mechanics to the motion of interfaces in random media. A key challenge is posed by the non-convex structure of the models, giving rise to rough energy landscapes and the emergence of complex physical phenomena. B) We establish generalizations of homogenization in the absence of scale separation, a problem naturally posed in the framework of random media. By developing new high-dimensional approximability results, we will contribute to uncertainty quantification and the design of numerical homogenization schemes with lower computational complexity. C) We develop a theory of stability and approximability of interface evolution problems past topology changes, a setting in which randomness may lead to the regularization of ill-posed evolutions and thereby allow for the derivation of error estimates for numerical approximation schemes. By relying on energy methods, we avoid the use of comparison principles, greatly enhancing the scope of applicability of our theory.

Keywords: pdes with multiple scales,stochastic homogenization,nonlinear material models,numerical homogenization schemes,interface evolution equations,phase field approximations