Norsk Regnesentral Norwegian Computing Center

Norsk Regnesentral Norwegian Computing Center

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Last update: Apr 4, 2024 Last update: Apr 4, 2024
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Legal residence:Norway
Types:NGONGO
Funding agencies:EC, Other, Government, FP7, Horizon 2020, Horizon Europe
Sectors:Banking, Energy, Finance & Accounting, Health, Ind ... See moreBanking, Energy, Finance & Accounting, Health, Industry, Commerce & Services, Information & Communication Technology, Public Administration, Research
Nr. of employees:51-200
Sanctions:Check now
Status:Active

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About

Norsk Regnesentral (Norwegian Computing Center, NR) is a private, independent, non-profit foundation established in 1952. NR carries out contract research and development projects in the areas of information and communication technology and applied statistical modeling.

NR is famous for inventing the first object oriented programming language Simula in the 1960s. 

NR is located at the campus of University of Oslo. The research groups are:

  • The Department of Applied Research in Information Technology at the Norwegian Computing Center offers applied research within multimedia, information security, universal design, and e-inclusion. Multimodality is a key strategy to usable data services, and we participate in projects handling multiple devices including mobile phones, PCs, and digital television. Our researchers have high scientific competence and broad industry experience.
  • The SAMBA department has comprehensive theoretical and practical knowledge in the fields of statistics, image analysis and patteren recognition. We are one of Europe's largest and most competent groups within applied statistics and statistical-matematical modelling. Many calculations involve uncertainty and the accurate calculation of this parameter is an important speciality.
  • The SAND department was established in 1984. It is a significant international contributor to research and services within reservoir description, stochastic modeling and geostatistics for the oil industry. Our primary goal is to use statistical methods to reduce and quantify risk and uncertainty. 

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