Senior Research Software Engineer

Meiert Grootes


Meiert studied physics and mathematics at the Christian-Albrechts-University of Kiel in Germany, specializing in Astrophysics and focusing on the study of accretion disks around super-massive blackholes using grid-based hydrodynamical simulations in FORTRAN for his master’s thesis.

Thereafter, he obtained a PhD in Astrophysics from the University of Heidelberg and the Max-Planck-Institute for Nuclear Physics, with research focusing on the processes driving galaxy evolution and their interplay with the cosmic web of dark matter filaments. As a member of the Galaxy And Mass Asembly (GAMA) Survey consortium, he developed data analysis pipelines for the survey’s UV photometry, machine learning based galaxy classification techniques, and radiative transfer modelling techniques, combining these for his research based on the statistical analysis of GAMA’s galaxy sample.

After defending his thesis in 2013, he expanded on this line of research as a post-doctoral fellow at the Max-Planck-Institute for Nuclear Physics and as an independent fellow at the European Space Agency, combining multi-wavelength photometric and spectroscopic data with Bayesian MCMC models directly probing dark matter halo structures using weak-lensing measurements.

Meiert joined the Netherlands eScience Center in 2018, and has since been working on projects ranging from macro-ecology using massive LiDAR point cloud data sets to machine learning supported close reading for humanities scholars. Currently, his overall interests lie in combining data pipelines, machine learning, and statistical modelling techniques with applications in earth observation and environmental science, (astro)physics, and humanities.

Key skills

  • Data Handling & Access
  • Data Pipelines
  • Data Analytics
  • Statistical Modelling
  • Machine Learning
  • Physics & Astrophysics
  • Scientific Methods