Climate Uncertainty Quantification Postdoc
Where You Will Work
Located in northern New Mexico, Los Alamos National Laboratory (LANL) is a multidisciplinary research institution engaged in strategic science on behalf of national security. LANL enhances national security by ensuring the safety and reliability of the U.S. nuclear stockpile, developing technologies to reduce threats from weapons of mass destruction, and solving problems related to energy, environment, infrastructure, health, and global security concerns.
What You Will Do
The postdoc position will involve multi-model uncertainty quantification of climate system feedbacks using hierarchical Bayesian methods, complex computer model output, simplified climate models, and observational data.
Some of the largest uncertainties in climate prediction involve system feedbacks that amplify or suppress warming from the greenhouse effect, such as changes in clouds, water vapor, and snow and ice. Complex climate models disagree on the strength and spatial distribution of these feedbacks.
The postdoc will develop new statistical methods for multi-model uncertainty quantification of climate feedbacks. The approach involves calibrating the parameters of a reduced stochastic model to the output of complex climate simulations as well as observational data.
The project offers the opportunity to publish novel statistical research in the areas of uncertainty quantification for combining multiple models, calibration of computer models with multivariate space-time outputs, and development of statistical methodology that incorporates underlying physical dynamics with data-driven modeling. The postdoc will collaborate closely with climate scientists and numerical modelers.
What You Need
Key Position Requirements; Experience and knowledge in:
* efficient estimation of large hierarchical Bayesian models
* space-time analysis
* dimension reduction
* computer programming in R, C/C++, and/or Fortran
Desired Skills:
Additional experience in one or more of the following areas:
* Bayesian calibration of complex numerical simulations, including treatment of model discrepancy (structural error)
* parameter estimation in deterministic or stochastic dynamical systems (differential equations)
* high-dimensional parameter estimation; numerical modeling; physical or environmental science
In addition to technical skills, the postdoc should be able to work well in a highly interdisciplinary team, have good communication skills, and an interest in applied scientific problems requiring novel statistical methodologies.
* Education: A Ph.D. in statistics, machine learning, or a closely related field completed within the past five years or soon to be completed.
* Notes to Applicants: In addition to applying on-line, please send a curriculum vitae, contact information for three references, 1-page statement of research interests, and a cover letter summarizing relevant qualifications and research and career goals to Nathan Urban (Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo.), with "Statistics Postdoc" as the subject line.
* Pre-Employment Drug Test: The Laboratory requires successful applicants to complete a pre-employment drug test and maintains a substance abuse policy that includes random drug testing.
Candidates may be considered for a Director's Fellowship and outstanding candidates may be considered for the prestigious Marie Curie, Richard P. Feynman, J. Robert Oppenheimer, or Frederick Reines Fellowships.
For general information to the Postdoc Program go to http://www.lanl.gov/careers/career-options/postdoctoral-research/index.php
Please apply for this position online at: http://bit.ly/IRC29835 or visit http://careers.lanl.gov and reference vacancy IRC29835. EOE
