Summer Schools

SCI-BUS, SHIWA, EDGI joint Summer School on Workflows and Gateways for Grids and Clouds

Success of the SCI-BUS, SHIWA, EDGI joint Summer School on Workflows and Gateways for Grids and Clouds

Budapest, Hungary
2-6 July 2012

The joint Summer School on Workflows and Gateways for Grids and Clouds was co-organised by three current European Union projects, SCI-BUS, SHIWA and EDGI, to show various aspects of cloud and grid computing. The school has attracted a total of 51 participants from 25 institutes. The tutors and students came to Budapest from 16 different countries.

Joint EGEE and SEE-GRID Summer School on Grid Application Support

Budapest, Hungary
June 25 - 30, 2007.

The need for intelligent, Internet based inter-organizational collaborations is stronger than ever. Advanced results in science can be achieved by sharing and using scientific instruments, computers, knowledge and databases in a collaborative fashion. Companies can survive on rapidly changing markets only if their e infrastructures can be swiftly adapted to new challenges, if their IT systems can be easily reorganized into more profitable structures.

Joint Regional CE EGEE and SEEGRID-2 Summer School on Grid Application Support

Budapest, Hungary
3-8 July, 2006

Researchers from different fields of sciences have already realised the need for tools that foster collaboration with colleagues in order to achieve new results by the sharing available computational and data resources, scientic instruments. Frontiers of science can be expanded nowadays only by using enormous amount of computing power and data storage.

GRID'05 EGEE Summer School

Budapest, Hungary
July 11-16, 2005

Scientist from the area of biology, chemistry, physics, astronomy, geosciences etc.., have already realised that they need efficient ways to collaborate with their colleagues all over the world to achieve new results by sharing the available computational resources, databases and scientific instruments. Frontiers of science can be expanded nowadays only by using enormous amount of computing power and data storage.

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