Project description

We are searching for outstanding individuals to conduct original research on modelling and/or analysis of photometric datasets for dark energy studies using statistical inference and  machine learning methods. The project is funded by a Wallenberg Foundation research project grant to Hiranya Peiris, Jens Jasche, Ariel Goobar, Jesper Sollerman, and Matthew Hayes. The positions are focussed on our involvement in the Legacy Survey of Space and Time (LSST) and the LSST Dark Energy Science Collaboration, preparing for upcoming data from the Vera C. Rubin Observatory.

The successful candidates will be part of the Oskar Klein Centre for Cosmoparticle Physics (www.okc.albanova.se) in Stockholm, a rich scientific environment that comprises more than a hundred researchers working in both theory and experiment in the fields of astronomy, astrophysics and particle physics at both Stockholm University and the Royal Institute for Technology. The OKC hosts a vibrant research program on dark matter, dark energy, transient and multimessenger astrophysics, structure formation, and related particle physics questions. Postdoctoral associates are also welcome to participate in Scientific Programs at Nordita, the Nordic Institute for Theoretical Physics, which bring together groups of leading experts to work on specific topics for extended periods.

Main responsibilities

The positions involve original research on cosmic large scale structure or time-domain cosmology, broadly related to testing fundamental physics and cosmology with LSST. Daily responsibilities will include developing and applying novel statistical and machine learning techniques, writing production-level code for analysis of LSST data and precursor datasets, and/or preparing for the coming LSST data by analyzing precursor surveys. Experience in astronomical image analysis or cosmology is desirable, but not essential. Experts in other relevant fields, especially those with artificial intelligence and machine learning backgrounds are also welcome to apply.

 

Ref.nr SU FV-3387-20

Sista ansökningsdag: 2021-15-01

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