Meiert Grootes studied physics with a minor in mathematics at the Christian-Albrechts-University of Kiel, Germany, specializing in astrophysics. His diploma thesis focussed on studying the evolution of accretion disks around super-massive blackholes exploring the effect of the host galaxy potential and generalizations of Newtonian gravity; work that was based on grid-based hydrodynamical simulations in FORTRAN.

For his PhD he went to Heidelberg, Germany, also becoming a member of the Galaxy And Mass Assembly survey consortium (GAMA) for which he designed and implemented the bespoke UV photometry pipeline. The further focus of his research initially lay on developing a star-formation rate unbiased classifier of galaxy morphology and a method of applying radiative transfer models to recovering the intrinsic spectral energy distribution of large statistical samples of galaxies, then shifting to applying these tools to empirically probing the fundamental process of gas-accretion by galaxies, commonly referred to as gas-fuelling.

After defending his thesis in 2013, he accepted a position as postdoctoral researcher at the Max-Planck Institute for Nuclear Physics, where he continued his research into gas-fuelling, focussing on empirically constraining how and to what extent the larger scale environment impacts the ability of satellite galaxies to accrete gas. Using the full multi-wavelength photometric and spectroscopic database and MC models they showed that, contrary to the standard paradigm, their gas-fuelling is largely unaffected.

In 2015 he then joined the European Space Agency as an independent fellow, where he focussed on the evolution of the dust content of galaxies, as well as on combining weak-lensing based direct constraints of galaxy dark matter halo mass using Bayesian MCMC models with his established methods to probe gas-fuelling. Focussing on the halo mass dependence of gas fueling for central galaxies and the evolution of the relation between galaxy star formation rate and stellar mass, one of the fundamental empirical relations of extragalactic astrophysics.

Together with his collaborators he presented evidence for tension between the currently favored model and the data available from modern surveys and have provided the basis for a sensitive test of the model with upcoming observational facilities such as the James Webb Space Telescope.

Key skills

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

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