Our doctoral students at HIDA Trainee Network
Hu Zhao
Project
Python-based Package for Probabilistic Simulation and Uncertainty Quantification for Earth Surface Processes
Home institute
AICES, RWTH Aachen University
Host institute
High-performance computing department, German Aerospace Center (DLR)
Summary
The aim of this project is to leverage innovative software engineering technologies of the DLR-HPC group in order to improve the quality and efficiency of our Python-based probabilistic package for forward and inverse uncertainty quantification of earth surface processes, and further to integrate our application-oriented package with common data science ecosystem.
Anna Simson
Project
Towards real-time data integration on-board ice melting technologies - Combining historical ice coring data, sensor data on-board melting probes and data from process simulations into a unified description of the ambient ice
Home institute
AICES, RWTH Aachen University
Host institute
Prof. Dr. Olaf Eisen, Institution: AWI (Alfred Wegener Institute)
Summary
The goal of the project is to combine data from ice cores, from sensors on-board melting probes, and from process simulations into a unified description of the ambient ice. The research stay at the AWI is devoted to assemble and scout the ice coring data sets that have been acquired and processed by the Glaciology Section at the AWI. The combined database will be used to develop methods for the real-time data integration on-board ice melting technologies.
Lisa Beumer
Project
Data Science in Nuclear Verification – Extracting Verification-relevant Information from Geospatial Big Data
Home institute
Institute of Energy and Climate Research - Nuclear Waste Management and Reactor Safety (IEK-6), Forschungszentrum Jülich
Host institute
German Aerospace Center (DLR), Earth Observation Center (EOC), Remote Sensing Technology Institute (IMF), Department EO Data Science
Summary
The objective of the project is to investigate the possibilities of data science driven satellite imagery processing in the context of nuclear verification. Data science methods for extracting relevant information and knowledge from big data archives of earth observation data will be identified and further developed.
The DLR-IMF's EO Data Science Department focuses on big data analytics, data intelligence and knowledge discovery, by developing advanced model-based processing algorithms and further exploring AI for EO.
The research stay aims at leveraging the capabilities and experiences of EO Data Science to further enhance data analytics for nuclear verification developed so far.
Leonardo Rydin Gorjao
Project
PowerDynSys – Power Dynamical Systems, a python package for dynamical system stability in power-grid systems
Home institute
IEK-STE, FZJ / Institute for Theoretical Physics, University of Cologne
Host institute
German Aerospace Center - Institute of Networked Energy Systems (DLR-VE)
Summary
The goal of this project is to design an efficient, open-source Python library for numerical simulations of networked power systems which can easily be combined with existent power-grid models. The DLR-VE has been at the forefront of modelling and documenting networked power-grid systems in open source models. The target is to have a functional, easy-to-use ODE solver for commonly employed power-grid dynamics models, accessible to academic and industrial applications.
