Project description

We are searching for an outstanding individual to conduct original research on the origin and evolution of cosmic structure using cosmological survey data and novel machine learning methods. Funded by a Simons Foundation grant to Jens Jasche the project will be part of a new collaboration on “Learning the Universe” (www.learning-the-universe.org), focusing on developing and applying novel Data Science and Machine Learning techniques to reconstruct the initial conditions of our universe and test fundamental physics with current and next-generation cosmological surveys.

The successful candidate will be a member of the Simons Collaboration with ample opportunities for multidisciplinary collaborations with researchers of the participating institutions (Columbia University, Lawrence Berkeley National Lab, Harvard University, Flatiron Institute, Institut Astrophysique de Paris, Université de Montreal, Princeton University, Carnegie Mellon University, MPA Garching).

In addition, the successful candidate will be part of the Oskar Klein Centre for Cosmoparticle Physics (www.okc.albanova.se/) in Stockholm. This rich scientific environment comprises more than a hundred researchers working in both theory and experiment in astronomy, astrophysics, and particle physics at 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 brings together leading experts to work on specific topics for extended periods.

The successful candidate will further be a member of our Aquila consortium (www.aquila-consortium.org), an international research collaboration developing novel data science techniques to study fundamental physics with cosmic structures.

Main responsibilities

The positions involve original research on developing and applying novel data science techniques to study the cosmic large-scale structure in surveys. Daily responsibilities will include working with our algorithm for Bayesian Origin Reconstruction from Galaxies (BORG), developing novel machine learning techniques, and analysing cosmological survey data. The position also involves frequent travel to our partner institutions in Europe and the United States.

 

Ref. No. SU FV-3441-21

Closing date: 15 January 2022

Complete information here.