The O'Shea group is part of a collaboration that is developing an exascale version of the Enzo cosmology code, and is looking for a postdoctoral researcher who will perform research in theoretical galaxy formation, and will also participate in Enzo-E code development in support of this research. The successful applicant will collaborate with researchers at MSU and in the Enzo collaboration, and will have the opportunity to lead their own projects. The specific area of research is flexible, and depends on the candidate's interests (e.g., high redshift galaxy formation, the circumgalactic medium, quenching, dwarf galaxy evolution, etc.). There will also be opportunities to mentor undergraduate students in projects related to this work and to participate in other professional development activities such as teaching, grant-writing, and public outreach. Applicants are expected to have a PhD in astrophysics, astronomy, physics, or a closely related field, and have significant expertise in computation. In addition, applicants should either have experience in running and analyzing astrophysical simulations of any sort or experience in parallel code development. Knowledge of the C++ programming language is preferred.Experience with Enzo, Enzo-E, or adaptive mesh simulations is not required. Review of applications will begin on December 16, 2019, and will continue until the position has been filled.
B.S., 2000, Engineering Physics, University of Illinois in Urbana-Champaign (UIUC)
M.S., 2002, Physics, University of Illinois in Urbana-Champaign (UIUC)
Ph.D., 2005, Physics, University of Illinois in Urbana-Champaign (UIUC)
Brian O'Shea received his B.S. in engineering physics at the University of Illinois in Urbana-Champaign (UIUC) in 2000, and his Ph.D. in physics from UIUC in 2005 (with 2002-2005 being spent as a graduate student in residence at the Laboratory for Computational Astrophysics at UC San Diego and in the Theoretical Astrophysics Group at Los Alamos National Laboratory). Following that, he was a Director's Postdoctoral Fellow at Los Alamos National Laboratory, with a joint appointment between the Theoretical Astrophysics Group and the Applied Physics Division.
Since 2008, he has been a member of the faculty at Michigan State University, with a joint appointment between the Department of Physics and Astronomy, Lyman Briggs College, and the National Superconducting Cyclotron Laboratory (assistant professor 2008-12, associate professor 2014-present). O'Shea is a computational and theoretical astrophysicist studying cosmological structure formation, including galaxy formation and the behavior of the hot, diffuse plasma within galaxy clusters. He is also a co-author of the Enzo AMR code, an expert in high performance computing, and an advocate for open-source computing and open-source science. He has authored or co-authored more than 50 peer-reviewed journal articles in astrophysics, computer science and education research journals.
• Theoretical & Computational Astrophysics: (Inter)Galactic Medium Interaction & Chemical Evolution.
• Computational Science: Scientific Visualizations, Open-source Software Development.
• Education: Physics and Computational Science, Student Problem-solving, Curriculum Reform.
Applications are invited for a post-doctoral researcher in STEM education to join Michigan State’s Department of Computational Mathematics, Science, and Engineering to study introductory computational science courses. The courses focus on teaching computational modeling, data analysis, and programming. The researcher would work in CMSE on projects related to understanding student learning and engagement in these courses as well as be able to pursue their own research interests as they pertain to computational science education. The researcher will be supervised by Dr. Devin W. Silvia and will have the opportunity to collaborate with Dr. Danny Caballero, Dr. Brian O’Shea, and others both inside and outside of CMSE. There may also be opportunities to mentor undergraduate students in projects related to this work.
SS-18: CMSE 402 Visual Scientific Datasets
Click "Teaching" link to see past courses.