Hi! I am Amirpasha

Geoscience ML / AI HPC  Open data & FAIRness  Workflow design

Amirpasha Mozaffari

I am working as Data Manager in Jülich Supercomputing Centre (JSC) at Jülich research centre (FZJ) and PhD candidate in University of RWTH Aachen in the field of hydrogeophysics.

Data manager in Jülich Supercomputing Centre (JSC),
Research Center Jülich, Germany



Jun 2019 - Present , Germany

  • Data pipeline and workflow management in HPC
  • Machine learning application in Geosciences
  • FAIR and reproducible digital object in Earth System Sciences

Feb 2015 - Jun 2019 , Germany

  • Numerical and statistical analysis of complex environmental data (Synthetic and Experimental data)
  • Development and optimization of high-performance numerical modeling algorithms
  • Developing analytical solutions for shortcomings in practical environmental problems

Jun 2014 - Jan 2015 , Germany

  • Effect of borehole design on electrical impedance tomography measurements

Talks & Posters

RDA plenary 15 - 2020
Virtual poster session:
FAIRness in Air Quality and Weather forecast
Poster session
NIC symposium 2020
On the use of containers for machine learning and visualization workflows on JUWELS
Poster - Here
PyStager in ESM forum 2020
Presentation about PyStager application for pre/post processing and staging on HPC
Presentation - Here
RDA Germany 2020
FAIRness in Air Quality and Weather forecast
Poster - Here
CWFR workshop June 2021
CWFR Working Meeting on Jupyter Usage / Use case in Weather forecast by DeepRain project
Poster - Here



Deep Rain is a project that aims to advance the possibility of using deep learning to improve small-scale rain forecast. I am responsible for staging more than 450 TB data weather data for further processing with the HPC system.


IntelliAQ is a European project (ERC grant) that aims to use deep learning to provide services for air quality forecasting. I am responsible for data preparation and staging.


MAELSTROM is a large-scale R&D project, aiming to fundamentally improve weather and climate prediction. It will join the powers of HPC and ML to cope with the extreme complexity inherent in weather and climate forecasts.