Project description

We are searching for outstanding individuals to conduct original research on computational modelling and/or advanced machine-learning for analysis of photometric datasets. We are also looking for theoretical physicists interested in foundational understanding of deep learning methods. Up to three positions are available.

The successful applicants will be part of the European Research Council Advanced Grant CosmicExplorer project, led by Hiranya Peiris. The positions are also partially funded by an award from the Goran Gustafsson Foundation. The work will prepare the path for the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), and will be carried out within the LSST Dark Energy Science Collaboration.

The successful candidates will be part of the Oskar Klein Centre for Cosmoparticle Physics (http://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, galaxy evolution, 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 in cosmology, broadly related to testing fundamental physics with LSST. Daily responsibilities will include computational modelling/simulation, 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.

A different research profile can include the theoretical investigation of deep learning, e.g., using effective field theory techniques. Experts in other relevant fields, especially those with experience in explainable artificial intelligence, are also welcome to apply.

 

Ref. No. SU FV-3326-21

Closing date: 15 January 2022

Complete information here.