The MIT Kavli Institute for Astrophysics and Space Research (MKI) seeks a postdoctoral associate for general computational galaxy formation research working under the direction of Prof. Mark Vogelsberger. His research group is broadly interested in galaxy formation and evolution simulations ranging from large to small scales within the early to present-day Universe.
The successful applicant will be encouraged to pursue independent research projects and to collaborate with other researchers at MKI. Previous experience in high-performance computing and code development is advantageous, especially related to large-volume or high-resolution cosmological hydrodynamics simulations. The ideal candidate will pursue novel applications of astrophysical codes and contribute to large simulation collaborations. Specific projects may relate to the epoch of reionization, alternative dark matter models (WDM, SIDM, FDM), cosmic dust physics, ISM physics, star formation, black hole feedback, or radiative transfer.
Applicants with a strong computational astrophysics background are encouraged to apply regardless of their prior scientific concentrations. The initial appointment is for two years with the extension to a third contingent on a performance review. Applicants must have a Ph.D. in astronomy, astrophysics, or a related field.
Applicants should submit a CV, publications list, and a brief past and future research statement (no more than 3 pages). Please identify, in your application, three references and their contact info -- letters will be requested from a subset of applicants. Completed applications and a list of references should be sent electronically to: Ms. Thea Paneth (firstname.lastname@example.org) by January 15, 2021. PDF format is strongly preferred. Questions may be directed to Prof. Vogelsberger (email@example.com).
MIT is an equal employment opportunity employer. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of race, color, sex, sexual orientation, gender identity, religion, disability, age, genetic information, veteran status, ancestry, or national or ethnic origin.