We are inviting applications for an Associate Research Scientist position in applied mathematics and scientific computing, emphasizing inverse problems in seismology and Bayesian analysis. The position is associated with the Modeling Earth from Atomic to Global Scale (MINERALS) project funded by the Gordon and Betty Moore Foundation. The project aims to advance and integrate three branches of geophysics – mineral physics, seismology, and geodynamics – to analyze and interpret the seismological structure of the deep Earth.

The successful candidate will work in a multidisciplinary team of senior and junior scientists to develop machine learning and Bayesian methods to interpret seismic tomographic models of the elastic structure of the deep Earth in terms of temperature and mineralogy. Using thermodynamic properties databases, these analytical methods will also be applied to predict equilibrium mineral assemblages in collaboration with an international team of geochemists and petrologists.

Candidates must have a PhD in scientific computing, applied math, mineral physics, geophysics, materials science, or a related field with a minimum of 2 years of relevant post-PhD experience

The position is initially for one year, possibly extending to four years. The position is available immediately and will remain open until it is filled. Applicants should send a CV, three recommendation letters, and a cover letter containing a research statement summarizing the candidate's past experiences and future goals. Applications should be submitted through the Academic Search and Recruiting (ASR) system on the Columbia University portal.

Columbia University is an Equal Opportunity Employer / Disability / Veteran

Pay Transparency Disclosure

The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training.  The above hiring range represents the University?s good faith and reasonable estimate of the range of possible compensation at the time of posting.

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