KCDS Virtual Open House - Project 04 Exploring the Potential of machine learning methods for improving operational hydrological forecasting and prediction (EPOforHydro)
Author
Uwe Ehret, Sebastian Krumscheid
Editor
Participating institute
KIT-Zentrum Mathematik in den Natur-, Ingenieur- und Wirtschaftswissenschaften (KIT-Zentrum MathSEE)
Institut für Wasser und Gewässerentwicklung (IWG)
Scientific Computing Center (SCC)
Genre
Description
Meet KIT Graduate School Computational and Data Science in our virtual open house! Find out more about the graduate school and current doctoral projects.
Duration (hh:mm:ss)
00:05:05
Series
Published on
31.03.2023
Subject area
License
Creative Commons Attribution – NonCommercial – ShareAlike 4.0 International
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| Container | mov,mp4,m4a,3gp,3g2,mj2 |
| Duration | 304.662000 s |
| Filename | DIVA-2023-28_mp4.mp4 |
| File Size | 18.204.285 byte |
| Frame Rate | 25 |
| Video Bitrate | 405456 bps |
| Video Codec | h264 |
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