Current International Grants
Some in close collaboration with ACK CYFRONET AGH as project partner or project coordinator (for further reading see: DICE projects)
|ALICE-AGH (1.11.2018-31.10.2023). Project partner: AGH
Collaboration ALICE – AGH University Science and Technology
The aim of the project is to support collaboration between experiment ALICE at CERN and AGH.
ALICE (A Large Ion Collider Experiment) is a general-purpose, heavy-ion detector at the CERN LHC which focuses on Quantum Chromodynamics
The main topic of collaboration focus on computer science support for data management including
data aggregation, merging and quality control.
Project website: https://twiki.cern.ch/twiki/bin/view/ALICEpublic/ALICEPublicResults
|TOTEM-AGH (1.7.2017-31.12.2018). Project partner: AGH, Department of Computer Science
Collaboration TOTEM – AGH University Science and Technology
Supported by the Polish Ministry for Science and Higher Education
The aim of the project is to support collaboration between TOTEM experiment at CERN and AGH. TOTEM (TOTal Elastic and diffractive cross section Measurement) is a precise proton spectrometer located at the CMS detector of LHC accelerator at CERN. Since 2013, Department of Computer Science AGH is a formal partner of TOTEM collaboration, participating in the research and development of experiment software. The main topic of collaboration focuses on development of software for off-line data analysis and applications of distributed and parallel processing tools for interactive data analysis.
Project website: http://totem.web.cern.ch/Totem/
|EurValve (2016-2019), Project reference: 689617 (project partner: ACK Cyfronet AGH)
Valvular Heart Disease currently affects 2.5% of the population, but is overwhelmingly a disease of the elderly and consequently on the rise. It is dominated by two conditions, Aortic Stenosis and Mitral Regurgitation, both of which are associated with significant morbidity and mortality, yet which pose a truly demanding challenge for treatment optimisation. By combining multiple complex modelling components developed in recent EC-funded research projects, a comprehensive, clinically-compliant decision-support system will be developed to meet this challenge, by quantifying individualised disease severity and patient impairment, predicting disease progression, ranking the effectiveness of alternative candidate procedures, and optimising the patient-specific intervention plan. This algorithmically-driven process will dramatically improve outcomes and consistency across Europe in this fast-growing patient group, maximising individual, societal and economic outcomes.
- A number of bilateral international cooperations and grants with:
- University of Amsterdam,
- University of Southern California, Information Sciences Institute
- Technical University of Munchen,
- University of Camerino,
- University of Vienna,
- University of Klagenfurt,
- University of Linz,
- University of Knoxville,
- Argonne National Laboratory,
- Supercomputing Institute of University of Minnesota
- Universite de Technologie de Compiegne /
Permanent collaboration with Academic Computer Centre CYFRONET-AGH