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UISAV

USTAV INFORMATIKY SLOVENSKEJ AKADEMIE VIED, VEREJNA VYSKUMNA INSTITUCIA
Country: Slovakia
16 Projects, page 1 of 4
  • Funder: EC Project Code: 873123
    Overall Budget: 303,600 EURFunder Contribution: 303,600 EUR

    The project aims to combine the expertise and resources of four academic groups working at the frontiers of material research and two SMEs, one engaged in producing commercial medical materials as well as having own research capacities. The Consortium plans to develop wound dressings which an be rated as a radical innovation in the area of chronic wound care. Our efforts will concentrate on the design of a new product which will combine diagnostic and therapeutic actions and facilitate wound healing, while at the same time minimize treatment costs and optimize wound management thus positively influencing patient's wellbeing. A range of new composites derived from naturally occurring materials, such as chitin and halloysite combined with colloids will be studied. The ensuing hybrid materials will be processed using micro- and nano-manipulating tools, including roll-to-roll and electrospinning technologies. A thorough characterization program will be undertaken in order to understand the molecular-level interactions between the composite components. The determination of materials structure will allow a complete understanding of how their properties originate so that they can be tailored to precisely fit their application. The dressing will feature diagnostic sensors capable of measuring pH, humidity, temperature and associated bacterial activity as well as a highly absorbent layer designed to drain the wound and release therapeutic agents. Each member of the Consortium is well-resourced in terms of facilities, infrastructure and technology transfer support so as to provide an ideal environment for the research program, the training of younger researchers and the exploitation of findings. Leveraging from nationally funded grants and actively engaging in research exchanges while seeking to produce a radical innovation in the area of 'smart' dressings the project will address all three key drivers of the knowledge-based society, namely research, education an innovation.

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  • Funder: EC Project Code: 859588
    Overall Budget: 4,009,380 EURFunder Contribution: 4,009,380 EUR

    COBRA aims to train the next generation of researchers to accurately characterize and model the linguistic, cognitive and brain mechanisms deployed by human speakers in conversational interactions with human interlocutors as well as artificial dialog systems. It relies on a cross-sectoral international network of 11 world-level academic research centers and 4 non-academic partners with 3 fast-developing SMEs and 1 world-level company. The partners' unique combined expertise and high complementarity will allow COBRA to offer 15 ESRs an excellent training programme as well as strong exposure to the non-academic sector in the emerging field of conversational brains. Training will cover scientific and technical skills, from the joint monitoring of brain and physiological activities in two or more people talking to each other to making multi-language databases, resources and findings available in open access, as well as transferable skills. The ESRs will conduct experimental and corpus studies on the alignment and prediction processes that make conversation between people both easy and fluent, across a large variety of communicational settings and in different languages, to better understand how these processes contribute to setting up brain-to-brain coupling relationships. Collaborative work with non-academic partners will foster the development of more effective and socially acceptable text-to-speech synthesizers, artificial dialogue systems, and social humanoid robots with high-level conversational skills. The project will open new career perspectives for ESRs with interdisciplinary training in language sciences, neuroscience and dialog systems on a very fast-growing digital market. COBRA’s training programme will also have major societal implications as it will concern aspects of the European citizens’ everyday life, from spoken interactions with machines to conversing in a non-native language.

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  • Funder: EC Project Code: 777533
    Overall Budget: 2,972,250 EURFunder Contribution: 2,972,250 EUR

    The PROCESS demonstrators will pave the way towards exascale data services that will accelerate innovation and maximise the benefits of these emerging data solutions. The main tangible outputs of PROCESS are five very large data service prototypes, implemented using a mature, modular, generalizable open source solution for user friendly exascale data. The services will be thoroughly validated in real-world settings, both in scientific research and in industry pilot deployments. To achieve these ambitious objectives, the project consortium brings together the key players in the new data-driven ecosystem: top-level HPC and big data centres, communities – such as Square Kilometre Array (SKA) project – with unique data challenges that the current solutions are unable to meet and experienced e-Infrastructure solution providers with an extensive track record of rapid application development. In addition to providing the service prototypes that can cope with very large data, PROCESS addresses the work programme goals by using the tools and services with heterogeneous use cases, including medical informatics, airline revenue management and open data for global disaster risk reduction. This diversity of user communities ensures that in addition to supporting communities that push the envelope, the solutions will also ease the learning curve for broadest possible range of user communities. In addition, the chosen open source strategy maximises the potential for uptake and reuse, together with mature software engineering practices that minimise the efforts needed to set up and maintain services based on the PROCESS software releases.

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  • Funder: EC Project Code: 101058593
    Overall Budget: 4,997,120 EURFunder Contribution: 4,997,120 EUR

    The AI4EOSC (Artificial Intelligence for the European Open Science Cloud) delivers an enhanced set of advanced services for the development of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) models and applications in the European Open Science Cloud (EOSC). These services are bundled together into a comprehensive platform providing advanced features such as distributed, federated and split learning; novel provenance metadata for AI/ML/DL models; event-driven data processing services or provisioning of AI/ML/DL services based on serverless computing. The project builds on top of the DEEP-Hybrid-DataCloud outcomes and the EOSC compute platform and services in order to provide this specialized compute platform. Moreover, AI4EOSC offers customization components in order to provide tailor made deployments of the platform, adapting to the evolving user needs. The main outcomes of the AI4EOSC project will be a measurable increase of the number of advanced, high level, customizable services available through the EOSC portal, serving as a catalyst for researchers, facilitating the collaboration, easing access to high-end pan-European resources and reducing the time to results; paired with concrete contributions to the EOSC exploitation perspective, creating a new channel to support the build-up of the EOSC Artificial Intelligence and Machine Learning community of practice.

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  • Funder: EC Project Code: 777435
    Overall Budget: 2,988,750 EURFunder Contribution: 2,988,750 EUR

    The key concept proposed in the DEEP Hybrid DataCloud project is the need to support intensive computing techniques that require specialized HPC hardware, like GPUs or low latency interconnects, to explore very large datasets. A Hybrid Cloud approach enables the access to such resources that are not easily reachable by the researchers at the scale needed in the current EU e-infrastructure. We also propose to deploy under the common label of “DEEP as a Service” a set of building blocks that enable the easy development of applications requiring these techniques: deep learning using neural networks, parallel post-processing of very large data, and analysis of massive online data streams. Three pilot applications exploiting very large datasets in Biology, Physics and Network Security are proposed, and further pilots for dissemination into other areas like Medicine, Earth Observation, Astrophysics, and Citizen Science will be supported in a testbed with significant HPC resources, including latest generation GPUs, to evaluate the performance and scalability of the solutions. A DevOps approach will be implemented to provide the chain to ensure the quality of the software and services released, that will also be offered to the developers of research applications. The project will evolve to TRL8 existing services and technologies at TRL6+, including relevant contributions to the EOSC by the INDIGO-DataCloud H2020 project, that the project will enrich with new functionalities already available as prototypes, notably the support for GPUs and low latency interconnects. These services will be deployed in the project testbed, offered to the research communities linked to the project through pilot applications, and integrated under the EOSC framework, where they can be further scaled up in the future.

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