Institute of Informatics
Targeting Real Chemical Accuracy at the Exascale (TREX)
Ako dosiahnuť reálnu chemickú presnosť v exascale ére
Imaging data and services for aquatic science (iMagine)
Cloudové služby pre spracovanie obrazových údajov pre vedy o vode
| Duration: |
1.9.2022 - 31.8.2025 |
| Program: |
Horizon Europe |
| Project leader: |
Ing. Tran Viet PhD. |
| Annotation: | iMagine provides a portfolio of free at the point of use image datasets, high-performance image analysis tools empowered with Artificial Intelligence (AI), and Best Practice documents for scientific image analysis. These services and materials enable better and more efficient
processing and analysis of imaging data in marine and freshwater research, accelerating our scientific insights about processes and measures relevant for healthy oceans, seas, coastal and inland waters. By building on the computing platform of the European Open Science Cloud (EOSC) the project delivers a generic framework for AI model development, training, and deployment, which can be adopted by researchers for refining their AI-based applications for water pollution mitigation, biodiversity and ecosystem studies, climate change analysis and beach monitoring, but also for developing and optimising other AI-based applications in this field. The iMagine compute layer consists of providers from the pan-European EGI federation infrastructure, collectively offering over 132,000 GPU-hours, 6,000,000 CPU-hours and 1500 TB-month for image hosting and processing. The iMagine AI framework offers neural networks, parallel post-processing of very large data, and analysis of massive online data streams in distributed environments. 13 RIs will share over 9 million images and 8 AI-powered applications through the framework. Having representatives so many RIs and IT experts, developing a portfolio of eye-catching image processing services together will also give rise to Best Practices. The synergies between aquatic use cases
will lead to common solutions in data management, quality control, performance, integration, provenance, and FAIRness, contributing to harmonisation across RIs and providing input for the iMagine Best Practice guidelines. The project results will be integrated into and will bring important contributions from RIs and e-infrastructures to EOSC and AI4EU. |
Inline evaluation of Li-ion batery electrod porosity using machine learning algorithms
Inline evaluácia pórovitosti elektród Li-ion batérií pomocou algoritmov strojového učenia
Integrated Technological and Information Platformfor wildfire Management (SILVANUS)
Integrovaná technologická a informačná platforma pre manažment lesných požiarov
| Duration: |
1.10.2021 - 31.3.2025 |
| Program: |
Horizon 2020 |
| Project leader: |
Ing. Balogh Zoltán PhD. |
| Annotation: | SILVANUS envisages to deliver an environmentally sustainable and climate resilient forest management platform through innovative capabilities to prevent and combat against the ignition and spread of forest fires. The platform will cater to the demands of efficient
resource utilisation and provide protection against threats of wildfires encountered globally. The project will establish synergies between (i) environmental; (ii) technology and (iii) social science experts for enhancing the ability of regional and national
authorities to monitor forest resources, evaluate biodiversity, generate more accurate fire risk indicators and promote safety regulations among citizens through awareness campaigns. The novelty of SILVANUS lies in the development and integration of
advanced semantic technologies to systematically formalise the knowledge of forest administration and resource utilisation. Additionally, the platform will integrate a big-data processing framework capable of analysing heterogeneous data sources
including earth observation resources, climate models and weather data, continuous on-board computation of multi-spectral video streams. Also, the project integrates a series of sensor and actuator technologies using innovative wireless communication
infrastructure through the coordination of aerial vehicles and ground robots. The technological platform will be complemented with the integration of resilience models, and the results of environmental and ecological studies carried out for the assessment of fire risk indicators based on continuous surveys of forest regions. The surveys are designed to take into consideration the expertise and experience of frontline fire fighter organisations who collectively provide support for 47,504x104 sq. meters of forest area within Europe and across international communities. The project innovation will be validated through 11 pilot demonstrations across Europe and internationally using a two sprint cycle. |
Smart Wound monitoring Restorative Dressings
Inteligentné diagnosticko-terapeutické náplasti
| Duration: |
1.1.2020 - 30.6.2024 |
| Program: |
Horizon 2020 |
| Project leader: |
RNDr. Bardošová Mária CSc. |
| Annotation: | 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. |
Language in the Human-Machine Era (LITHME)
Jazyk v ére človek-stroj
COnversational BRAins
Konverzujúce mozgy
| Duration: |
1.2.2020 - 31.1.2024 |
| Program: |
Horizon 2020 |
| Project leader: |
Prof.Mgr. Beňuš Štefan PhD. |
| Annotation: | The EU-funded COBRA project will train the next generation of researchers to accurately characterise and model the linguistic and cognitive brain mechanisms that allow conversation to unfold in both human-human and human-machine interactions. The first challenge will be to determine how alignment and prediction may both rely on and contribute to setting up brain-to-brain coupling relationships. The second will relate to the development of computational models of alignment and prediction for more socially-acceptable text-to-speech synthesisers, human-machine dialogue systems, and social robots. |
Artificial Intelligence for the European Open Science Cloud (AI4EOSC)
Umelá inteligencia pre EOSC
| Duration: |
1.9.2022 - 31.8.2025 |
| Program: |
Horizon Europe |
| Project leader: |
Ing. Tran Viet PhD. |
| Annotation: | 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. |
leveraging the European compute infrastructures for data-intensive research guided by FAIR principles (EuroScienceGateway)
využitie európskych výpočtových infraštruktúr pre výskum náročný na údaje riadený zásadami FAIR
| Duration: |
1.9.2022 - 31.8.2025 |
| Program: |
Horizon Europe |
| Project leader: |
Ing. Tran Viet PhD. |
| Annotation: | In the past decade, many scientific domains have been transformed into data-driven disciplines relying on the exchange and integration of internationally distributed data. Exploiting this data is still a laborious and largely manual task, prone to losses and errors, and increasingly specialised beyond most users technical capabilities. FAIR practices are encouraged but their adoption curve is steep. The needs for compute and data resources, tools, and application platforms are often domain-specific. Many scientists struggle to navigate this intricate ecosystem. Generally, researchers do not possess the computing skills to effectively use the HPC or Cloud platforms they need. Thus, new approaches are needed to enable all researchers, with widely ranging digital skills, to efficiently use the diverse computational infrastructures available across Europe, for asynchronous and for interactive applications.
EuroScienceGateway will leverage a distributed computing network across 13 European countries, accessible via 6 national, user-friendly web portals, facilitating access to compute and storage infrastructures across Europe as well as to data, tools, workflows and services that can be customized to suit researchers' needs. At the heart of the proposal workflows will integrate with the EOSC-Core. Adoption, development and implementation of technologies to interoperate across services, will allow researchers to produce high-quality FAIR data, available to all in EOSC. Communities across disciplines - Life Sciences, Climate and Biodiversity, Astrophysics, Materials science - will demonstrate the bridge from EOSC's technical services to scientific analysis.
EuroScienceGateway will deliver a robust, scalable, seamlessly integrated open infrastructure for data-driven research, contributing an innovative and customizable service for EOSC that enables operational open and FAIR data and data processing, empowering European researchers to embrace the new digital age of science. |
The total number of projects: 9
