Los Alamos National Laboratory Verification and Validation Scientist (Scientist 3) in Los Alamos, New Mexico
Vacancy Name: IRC75272
Job Title Verification and Validation Scientist (Scientist 3)
Location Los Alamos, NM, US
Organization Name XCP-8/Verification And Analysis
Minimum Salary 98900
Maximum Salary 165100
What You Will Do
The Computational Physics: Verification & Analysis group (XCP-8) at the Los Alamos National Laboratory (LANL) has an opening for doctoral-level career scientists/engineers with research interests and experience in the areas of code and solution verification, and validation assessments of models with computer codes. Our wide-ranging areas of interest include (but are not limited to) hydrodynamics, solid mechanics, material strength and damage, equation of state, reactive flow, high-energy-density physics, instabilities and turbulence, and radiation transport. We encourage mentoring for new staff to build skills quickly and continued publications/research are prized as well. This position is ideal for those who have completed post-doctoral work.
The selected candidate will be part of a multi-disciplinary team to assess the effect of numerical error, experimental error, and/or model insufficiency on simulation uncertainty in large-scale physics simulations. This requires assessing the quality of numerical algorithms and physical models that are implemented in multi-physics simulation codes developed at LANL to ensure the veracity of physical predictions involving complex systems. This involves collaborating with code developers, experimentalists, and other physics subject-matter experts in order to understand various errors associated with single physics sub-models and how these affect complex multi-physics simulation results. The successful candidate will evaluate whether numerical algorithms and/or physical models were implemented correctly in LANL codes, and quantitatively determine how well those implementations reproduce representative analytic solutions and/or experimental results. Where appropriate, the candidate will evaluate potential model insufficiencies, as well as evaluate the effect of coupling between physics sub-models on the overall model accuracy. The successful applicant should have a strong background in experimental science, engineering, mathematical or computational physics, or statistics. Experience with interpreted languages such as Python, Matlab, R or something similar is required, Python experience being the most desirable. Please visit XCP-8 https://www.lanl.gov/org/padwp/adx/computational-physics/verification-analysis/index.php .
This position may also include responsibility for the leadership of the Advanced Simulation & Computing (ASC) V&V program’s Verification project.
What You Need
Minimum Job Requirements:
Demonstrated ability to create new V&V metrics to analyze complex models like AMR, ALE, stochastic codes, etc.
Research-level expertise developing, advancing and using V&V methods for complex multi-physics simulations.
Experience using, developing and/or maintaining regression test suites.
Experience using and/or developing codes for physics or engineering applications.
Strong programming skills in Fortran, C++ and/or Python. Experience programming in Matlab or R may be substituted.
Familiarity with Unix.
Demonstrated ability to publish research in peer-reviewed journals or conference proceedings, communicate research to an interdisciplinary audience and work on an interdisciplinary team.
Demonstrate ability to perform independent research for at least 5 years after PhD.
Research-level expertise developing and/or applying verification methods to codes to assess the numerical error associated with code simulations.
Research-level expertise validating model implementations in simulation codes.
Proficiency with software engineering tools such as: version control systems; software repository systems; issue trackers; collaboration platforms; and continuous integration systems.
Experience using and/or developing LANL’s Advanced Simulation and Computing Program computational physics codes.
Familiarity with and demonstrated interest in LANL’s mission and research environment.
Familiarity with using High Performance Computing clusters.
Demonstrate mentoring skills with students and/or postdocs.
Ability to obtain a Q clearance.
Education Required: PhD in Physics, Computer Science, Engineering, Statistics, Machine Learning, or related field preferred. Postdoctoral research experience is preferred
Notes to Applicants: Please submit a detailed cover letter with your resume addressing all required and desired skills (please save cover letter with job number and name).
Clearance: Q (Position will be cleared to this level). Applicants selected will be subject to a Federal background investigation and must meet eligibility requirements
for access to classified matter.
Eligibility requirements: To obtain a clearance, an individual must be at least 18 years of age; U.S. citizenship is required except in very limited circumstances. See DOE Order 472.2 for additional information.
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.
Regular position: Term status Laboratory employees applying for regular-status positions are converted to regular status.
Internal Applicants: Please refer to Laboratory policy P701 for applicant eligibility.
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 firstname.lastname@example.org or call 1-505-665-4444 option 1.
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.
Our diverse workforce enjoys a collegial 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.
Compensation and Benefits:
Flexible work schedules
Exercise facility free for staff use
Choice of comprehensive medical plans
Paid maternity leave, sick time
Paid parental leave
401k match (100% up to 6% + kicker)
Fully vested in 401k day one
Los Alamos National Laboratory in Los Alamos, NM enjoys excellent weather, clean air 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. The short drive to work includes stunning views of the Valles Caldera as well as the Sangre de Cristo mountains. Many employees also live in the nearby state capital, Santa Fe, which is known for world-class restaurants, art galleries, and opera.
Appointment Type Regular
Contact Name Chavez, Raymond Ceasar
Req ID: IRC75272