Interdisciplinary research projects at the interface between natural science and data science
Topic A: "Arctic Environmental Data Analytics"– Gregor Pfalz
Topic B: "Data fusion using remote sensing data and machine/deep learning techniques to better understand present, past and future vegetation dynamics in Central Yakutia" – Femke van Geffen
The Helmholtz Einstein International Research School in Data Science (HEIBRiDS) is a cooperation project with the Einstein Center Digital Future (ECDF), Berlin’s universities and the six Helmholtz Centers in the capital region.
The different doctoral theses focus on topics from the fields of imaging, machine learning, modeling, innovative hardware concepts, visualization and sequencing. The interdisciplinary topics are formulated and supervised by a team consisting of two professors, one of whom is a member of the Helmholtz Association and one an ECDF member
HEIBRiDS can therefore rely on a unique environment that enables research, from different perspectives, into the core methods, algorithms and processes of digitalization, while at the same time transporting knowledge between different disciplines.
Topic A tries to reconstruct past and present relationships between climate change in the Arctic and ecosystem dynamics in northern lake systems, by developing a data analysis system designed for multivariate statistics on lake sediment core parameters.
The goal of Topic B, is to employ machine learning and deep learning methods to analyse data to gain better insights into the dynamics of the vegetation species and how these change over time. In order to accomplish this goal, various types of remote sensing data are used such as Sentinel-2 and Landsat 7/8 as well as drone data collected in the field. The ultimate goal is to develop a fusion method that can use the available data to create a comprehensive overview of vegetation dynamics of the past, present and future. .
Research focus: Arctic Lake System Dynamics (Topic A)
High-latitude Vegetation Change (Topic B)
Contact: Boris Biskaborn, Ulrike Herzschuh, Bernhard Diekmann, Gregor Pfalz, Femke van Geffen
Funding: HEIBRiDS Graduate School (2018 – 2022)