DescriptionThe Department of Statistics and Data Science at Carnegie Mellon University invites applicants for a two-year post-doctoral fellowship in simulation-based inference. The fellow will work with Prof. Cosma Shalizi of the department on developing theory, algorithms and applications of random feature methods in simulation-based inference, with a particular emphasis on social-scientific problems connected to the work of CMU's Institute for Complex Social Dynamics. Apart from by the supervisor, the fellow will also be mentored by other faculty in the department and the ICSD, depending on their interests and secondary projects, and will get individualized training in both technical and non-technical professional skills.QualificationsSuccessful applicants will have completed a Ph.D. in Statistics, or a related quantitative discipline, by September 2025, and ideally have a strong background in non-convex and stochastic optimization and/or Monte Carlo methods, and good programming and communication skills. Prior familiarity with simulation-based inference, social network models and agent-based modeling will be helpful, but not necessary.Application InstructionsApplicants should submit a combined cover letter and statement of research interests (of no more than four pages); a CV listing all their publications; and one paper or dissertation chapter as a PDF. They should also arrange for three letters of recommendation. All materials must be received by 15 December 2024.Equal Employment Opportunity StatementCarnegie Mellon University shall abide by the requirements of 41 CFR §§ 60-1.4(a), 60-300.5(a) and 60-741.5(a). These regulations prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities, and prohibit discrimination against all individuals based on their race, color, religion, sex, or national origin. Moreover, these regulations require that covered prime contractors and subcontractors take affirmative action to employ and advance in employment individuals without regard to race, color, religion, sex, national origin, protected veteran status or disability.
