Los Alamos National Laboratory

Position ID: 2687-POSTDOC [#18524]
Position Title: Numerical Methods, Differential Equations and Uncertainty Quantification Postdoc
Position Type: Postdoctoral
Position Location: Los Alamos, New Mexico, United States
Application Deadline: finished (2021/10/07, finished 2023/04/01, listed until 2022/09/30)
Position Description:    

*** this position has been closed. ***

Los Alamos National Laboratory (LANL) is a multidisciplinary research institution engaged in science and engineering on behalf of national security. We are seeking a resourceful and highly motivated Numerical Methods, Differential Equations and Uncertainty Quantification Postdoc to join the T-5 Applied Mathematics and Plasma Physics Group in our Theoretical Division to develop cutting-edge methods for numerical solution of very large partial differential equation system with an emphasis on uncertainty quantification. In this position, you will be expected to:

• Interact with scientists working in numerical methods, applied mathematics, data science, statistical physics, machine learning, optimal control and power system engineering in different organizations of the Laboratory • Research efficient methods for (1) computational solution for predicting the propagation of statistical uncertainty in partial differential equation systems on graphs and (2) mathematical optimization formulations for optimal control of dynamic systems that account for probabilistic constraints that effectively represent uncertainty

Minimum Requirements: • PhD in mathematics, applied statistics, electrical engineering, computational science or related field completed within the last 4 years • Proficiency in probabilistic reasoning and uncertainty quantification • Ability to conduct independent and collaborative research in a multi-disciplinary environment • Scientific/numerical programming experience in Julia, C++, Python, Java or MATLAB

Desired Qualifications: • Extensive experience and strong track record in scientific computing and numerical methods for differential equations • Experience with optimization software and packages such as ipopt, cplex, gurobi, etc. • Experience in solving practical science and engineering problems • Excellent interpersonal, oral and written communication skills • Ability to meet tight schedules and organize and prioritize tasks for effective achievements of project goals

We Are Delivering Scientific Excellence Los Alamos National Laboratory is more than a place to work. It is a catalyst for discovery, innovation and achievement. Professional development, work/life balance and a diverse and inclusive team foster lasting career satisfaction. Our onsite medical and fitness facilities, education assistance and generous compensation and benefits reflect our commitment to providing our people with all they need for personal and professional growth.

lanl.gov/careers 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.

We are not accepting applications for this job through MathJobs.Org right now. Please apply at https://lanl.jobs/los-alamos-nm/numerical-methods-differential-equations-and-uncertainty-quantification-postdoc/DD4639FAC6B94B599EE961D14C5D510C/job/ external link.
Postal Mail:
PO Box 1663
Los Alamos, NM 78229