Postdoctoral Position In Machine Learning Applied To Galaxy Morphology | [Mfh-218]

An international joint research program with participation from ESA, STScI, Galaxy Zoo, the Centro de Astrobiología (CAB), and the University of Toronto is looking for an astrophysicist to join for an initial 2-year phase to explore the application of innovative machine-learning techniques to improve our understanding of galaxy morphology.

The aim of the project is to develop and apply novel deep learning techniques to study the morphology of galaxies in different environments using archival and/or brand new data from the Hubble Space Telescope, James Webb Space Telescope, and Euclid.

With support from ESA's Data Science team, the candidate will use the ESA Datalabs platform to develop and deploy models based on the archival data from one or several ESA/NASA missions as well as algorithms developed by Galaxy Zoo.

The successful candidate will join the CAB, co-located at ESA's European Space Astronomy Centre campus near Madrid in Spain, and will have the opportunity for collaborative research trips to our partner institutes at STScI and the University of Toronto.

Duration: Initially two years, extension depending on available funding.

Required skills: Preferably research experience in galaxy evolutionExperience with the application of machine learningStrong communication skillsQualifications: PhD in astrophysics or comparable.

More info: Start date: Q4 2023For more information please contact (), with CC: to Jan Reerink
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