finished (2022/09/13, finished 2023/06/17)University of Michigan Postdoctoral Position in Machine Learning and Software Development for Cosmology The BLISS Project, led by Dr. Jeffrey Regier (U Michigan, statistics) and Dr. Camille Avestruz (U Michigan, physics), is an NSF-funded interdisciplinary initiative to develop Bayesian software for analyzing astronomical data. This project aims to advance numerous areas of computational cosmology, including strong lensing detection and measurement, galaxy cluster finding and characterization, and redshift estimation. Additional aims of this project include jointly analyzing heterogeneous photometric and spectrographic data sources (“multi-domain analysis”) and integrating our software with community software tools such as Astropy and the software stacks of the Rubin Observatory’s Legacy Survey of Space and Time (LSST) and the Dark Energy Spectroscopic Instrument (DESI) collaboration. The BLISS Project invites applications for a Postdoctoral Researcher position at the University of Michigan, hosted by either the Physics Department or the Statistics Department. Candidates should have received a PhD in astronomy, physics, or a related field before starting the position, and they should have prior experience in software engineering. The ideal candidate will have prior experience with deep learning software (e.g., PyTorch or TensorFlow) and collaborative code development through GitHub. The successful applicant will collaborate with an outstanding team of statistics and physics graduate students and external collaborators in various large survey collaborations, including the LSST Dark Energy Science Collaboration and DESI. This position will provide the successful candidate with an opportunity to further their knowledge of statistics, deep learning, and high-performance computing. This position will also provide experience in a lead software development role, and the opportunity to publish in astronomy journals like MNRAS and ApJ and in statistics journals like AoAS. The anticipated start date of this position is September 1, 2023; but there is some flexibility in the start date, so beginning the position any time during 2023 is possible. The appointment will last for an initial period of one year, with renewal for a second and third year contingent upon progress and funding. The position includes a competitive salary, research/travel funds, and access to high-performance computing facilities. Our preference is to hire a candidate who will live in Ann Arbor—a charming university town with the resources of a city many times its size. However, we are open to hybrid or remote-work arrangements. If you require a hybrid or remote-work arrangement, please mention this in your cover letter. Those interested in this position can apply online through mathjobs: https://www.mathjobs.org/jobs/list/20652. Applications received by December 10, 2022 will receive full consideration. Please submit your CV (including a list of your publications and preprints), three reference letters, and a cover letter. Your cover letter should explain (1) your interest in the position, (2) your software development training and experience (include your github username if appropriate), and (3) any prior experience you have with Bayesian statistics and deep learning. You are also welcome to submit any additional materials (e.g., a research statement or unreleased manuscripts) that may be relevant by appending them to your cover letter. Questions should be directed to Jeffrey Regier (regier@umich.edu) or Camille Avestruz (cavestru@umich.edu). This appointment is contingent on successful completion of a background screening. The University of Michigan is an equal opportunity/affirmative action employer. Women and minorities are encouraged to apply. This offer is contingent upon reporting your COVID-19 proof of vaccinations no later than one week before your appointment start date. You may request a medical or religious exemption; however, successful completion of the exemption process must occur prior to your start date. If you will not be up to date on your vaccinations at the time of hire (defined as receiving all recommended doses in the primary vaccine series and one booster when eligible), you may request an exemption. A temporary postponement may be requested if you are unable to receive the primary series or booster in your country of origin or current location. More information on this policy is available on the Campus Blueprint website. References: Scalable Bayesian Inference for Detection and Deblending in Astronomical Images |
)