1.3 | Machine learning for numerical modelling, data assimilation and data inversion | Double precision GPU with > 10 GB GPU memory (e.g., V100) | Nvidia driver, cuda, Python, R, Tensorflow and Keras libs, NetCDF support (+ ncview), Climate Data Operators (CDO), tmux | --
1.3 | Machine learning for numerical modelling, data assimilation and data inversion | Double precision GPU with > 10 GB GPU memory (e.g., V100) | Nvidia driver, cuda, Python, R, Tensorflow and Keras libs, NetCDF support (+ ncview), Climate Data Operators (CDO), tmux | --
2.4 | Full waveforms inversion for seismic wave propogation simulation | Single precison GPU with around 20 GB GPU memory | Nvidia driver,conda, hdf5, netcdf
2.4 | Full waveforms inversion for seismic wave propogation simulation | Single precison GPU with around 20 GB GPU memory | Nvidia driver,conda, hdf5, netcdf
3.6 | Atomistic simulation of (Geo-)materials | DP GPU preferred over SP | Nvidia drv, cuda, Python, MKL | for us at least 192 GB main memory, preferably more. Also, why not consider AMD EPYC instead of xeon? From a price/performance point of view
3.6 | Atomistic simulation of (Geo-)materials | DP GPU preferred over SP | Nvidia drv, cuda, Python, MKL | for us at least 192 GB main memory, preferably more. Also, why not consider AMD EPYC instead of xeon? From a price/performance point of view
1.4 | Machine learning for Sentinel-1 SAR data | Double precision GPU with > 10 GB GPU memory | Nvidia driver with cuda, tensorflow, Python | !
