The Applied Mathematics and Plasma Physics group (T-5) is seeking an outstanding post-doctoral candidate in the field of Applied Mathematics and/or Statistics. The applied research will focus on uncertainty quantification and reduced order modeling in the context of applied geophysical modeling and geomaterials. Research directions may include development of reduced order modeling techniques for data assimilation (DA), Bayesian hierarchical modeling, as well as algorithmic and/or high performance computing development. Research will be centrally focused on combining physical models of subsurface flow and fracture models with simulated and/or experimental data to constrain model parameter estimates (inverse problems) and predictions of various quantities of interest. A successful candidate is expected to publish in high-impact journals, as well as present their research at conferences and workshops.
T-5’s research focuses in numerical analysis, mathematical modeling, and plasma physics theory and modeling. Current activities in the group include research in numerical analysis and algorithm development for linear and nonlinear PDEs, plasma physics, computational physics and biophysics, methods of uncertainty quantification, and network analysis. The research in T-5 is funded by the DOE Office of Science, the nuclear weapons program, and a variety of other federal funding agencies and industrial partners, and typically involve close collaborations within and outside the Laboratory.
What You Need
Candidates with a strong background/experience in the following areas are invited to apply:
· Strong background of applied mathematics, computational geophysics, statistics or related area.
· Background in any of the following sensitivity analysis or uncertainty quantification methods such as MCMC, data assimilation, nonparametrics, etc.
· Skilled in scientific computing and code development.
· Outstanding track record of peer-reviewed publications.
· Excellent oral presentation and writing skills
· Background in data assimilation, such as, Monte Carlo Methods, Ensemble Kalman filter (KalEnKF), 3D-Var/4D-Var.
· Experience with Linear and nonlinear dimension reduction (e.g., Dynamic Mode Decomposition, Proper Orthogonal Decomposition, Principal Component Analysis, and Kernel based methods).
· Familiarity with Machine learning approaches, such as Gaussian process modeling or probabilistic graph modeling.
· Background in Bayesian hierarchical modeling.
· Strong programming background (Python, C/C++, Fortran, R).
· Experience with parallel programing (MPI, OpenMP, etc.)
Education: A Ph.D. in Applied Mathematics, statistics, or related field completed within the last five years or soon to be completed.
Notes to applications: In addition to applying online, applicants should email a CV and a cover letter not exceeding one page briefly describing research interests, expertise and qualifications for the position advertised to Humberto C. Godinez (hgodinez@lanl.gov) and David Osthus (dosthus@lanl.gov). US Citizenship is not a requirement. Review of applicants will start immediately and continue until the position is filled.
Applicants should have a demonstrated ability to pursue research independently, as well as collaboratively as a member of a team, and a strong record of publication. This is a two-year position starting on or after August 1, 2017.
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.
Our diverse workforce enjoys a casual work environment focused on creative problem solving, where everyone’s opinions and ideas are considered. We are committed to work-life balance and personal/professional growth. Our creative and dedicated computational professionals are our greatest asset and we take pride in cultivating their talents, supporting their efforts, and enabling their success. Together we are advancing our national security mission.
Los Alamos National Laboratory in Los Alamos, NM enjoys excellent weather and outstanding public schools. This is a safe, low-crime, family-oriented community with frequent concerts and events as well as quick travel to many top ski resorts, scenic hiking trails, and mountain climbing.
Additional Details:
Position does not require a security clearance. Selected candidates will be subject to drug testing and other pre-employment background checks.
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 go to Postdoc Program.
New-Employment Drug Test: The Laboratory requires successful applicants to complete a new-employment drug test and maintains a substance abuse policy that includes random drug testing.
Equal Opportunity:
Los Alamos National Laboratory is an equal opportunity employer and supports a diverse and inclusive workforce. All employment practices are based on qualification and merit, without regards to race, color, national origin, ancestry, religion, age, sex, gender identity, sexual orientation or preference, marital status or spousal affiliation, physical or mental disability, medical conditions, pregnancy, status as a protected veteran, genetic information, or citizenship within the limits imposed by federal laws and regulations. The Laboratory is also committed to making our workplace accessible to individuals with disabilities and will provide reasonable accommodations, upon request, for individuals to participate in the application and hiring process. To request such an accommodation, please send an email to applyhelp@lanl.gov or call 1-505-665-4444 option 1.
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