Viktoria Pauw is part of the leading team of Environmental Computing (ENV) Team at Leibniz Supercomputing Centre (LRZ, part of the Bavarian Academy of Sciences and Humanities). She studied physics (major) and Computer Science (minor) and LMU in Munich. In 2016, she joined LRZ working in System Monitoring and Data Analysis. Her interests are focused on the interface of IT and domain science, particularly physics and environmental topics and the variety of methods and technologies that can enable scientists to follow their curiosities and broaden the possibilities of research in the 21st century. She is currently pursuing a Ph.D. degree in computational physics at LMU in the field of PIC simulations.
Selected Projects and Collaborations¶
- K2I (Kollektive x Künstliche Intelligenz)
Exploring the possibilities of collective data pooling and machine learning for analysing Non-Target Screening (LC-HRMS) Data of River water.
- GeoKW (Geo Kälte&Wärme)
Using large hydro-geological models coupled with energy optimization tools to explore the potential of geothermal energy usage for heating and cooling in the city of Munich.
- EXA4MIND (EXtreme Analytics for MINing Data spaces, Horizon Europe GA 101092944, lead Dr. J. Martinovič – IT4Innovations/VSB-TUO)
bridging the gaps between databases, HPC and FAIR data ecosystems, smart vineyards as a specific environmental-computing use case
- CoCoReCs (SeisSol als Community-Software für Reproduzierbare Computational Seismology)
Increasing Reproducibilty in Scientific Software Development through Continous Integration workflows and exploring the use of Containerized Applications in High Performance Computing
- SuperMUC(-NG) projects at the chair of Prof. Ruhl (LMU, Computational and Plasma Physics)
PIC simulation support for Exploring novel accelerator concepts using Laser-Plasma interaction to create high energy particles (2014-2018) see SuperMUC result book 2018, p.260 https://doku.lrz.de/files/10745976/10745983/1/1684599961997/2018_SuperMUC-Results-Reports.pdf and 2016, p.66, https://doku.lrz.de/files/10745976/10745986/1/1684599969577/2016_SuperMUC-Results-Reports-hires.pdf
Selected R&D Interests and topics¶
High Performance Computing
Continous Integration Workflows
Machine Learning and Data Analysis
Monitoring and Reporting