Applications are invited for a postdoctoral research position at the Johns Hopkins University’s Center for Astrophysical Sciences (CAS) in Baltimore, Maryland. The successful applicant will work with Dr. Michelle Ntampaka to develop a suite of machine learning and statistical tools to interpret cosmological large scale structure.
The decadal survey’s Pathways to Discovery identified the crucial role that machine learning (ML) could play in the next decade, leading to transformative discoveries from the decade’s rich, upcoming data sets. While ML has historically been touted as a black box that can generate order-of-magnitude improvements at the cost of interpretability, this does not need to be the case – modern techniques are making it possible to develop ML tools that improve results while still being understandable and leading to physical discoveries. This research program will develop a toolkit of understandable ML methods for interpreting detailed optical galaxy surveys from the Rubin Observatory and the Dark Energy Spectroscopic Instrument to explore the sigma-8 tension. This research will 1) produce a statistical census of cosmological information at small scales, probing techniques for describing how sub-Mpc structures correlate with the underlying cosmological model, 2) produce a low-scatter galaxy cluster dynamical mass proxy using symbolic regression to provide a closed-form, complementary framework for quantifying the abundance of massive clusters at low redshift, and 3) develop a deep learning approach for estimating galaxy cluster ellipticity, a major source of systematic error in weak lensing cosmological analyses.
The successful applicants will benefit from interactions with the Johns Hopkins University and Space Telescope Science Institute (STScI) research staff, a career mentoring program at STScI, and a stimulating work environment rich in colloquia, journal clubs, and symposia, hosted through both JHU’s CAS and STScI. This position will provide opportunities for collaboration with the Chesapeake Machine Learning and Astronomy research group (https://chesapeakemlastro.github.io/) and also with researchers at Space Telescope Science Institute, including Dr. John Wu, Dr. Javier Sanchez, and Dr. Rachael Beaton. Independent research in related areas will be encouraged and supported up to the 50% level pending satisfactory progress on the main project.
Applicants must hold a Ph.D. degree in Astronomy, Physics, or a related field at the start of the post-doctoral researcher position. Applicants with experience in the areas of cluster cosmology, galaxy clustering, machine learning, and/or statistical methods are especially well-suited to the position. Expertise using N-body and hydrodynamical cosmological simulations will be a plus.
The position is for two years, with a possible renewal for a third year subject to performance and availability of funds. The nominal start date is summer or early fall of 2024, but earlier start dates are possible. A competitive benefits package is provided by Johns Hopkins University. We especially welcome applications from women, minorities, veterans, LBGTQ+ people, and other members of underrepresented groups.
The following materials are requested in a single PDF file: a brief cover letter detailing your research interests and relevant expertise, a curriculum vitae, a list of publications, and a summary of previous and current research (limited to 3 pages). Also, applicants should arrange for three confidential letters of reference to be submitted on their behalf. Complete applications received by December 7, 2023, will receive full consideration, but applications will be accepted until the position is filled.
Reference letters should be submitted by December 18, 2023 for full consideration.
Inquiries about the position may be sent directly to Dr. Ntampaka at [email protected]
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