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Job #: R-00015502
Location: Morgantown, WV
Category: Research Scientist
Schedule (FT/PT): Full Time
Travel Required: No
Shift: Day
Potential for Telework: No
Clearance Required: None
Referral Eligibility: Ineligible
Group: Civil

Description

Job Requisition:

Computational Materials Scientist – Multiphysics

Job Description:

An opportunity exists for a computational scientist to develop and utilize computational multiphysics models and data analysis tools to support NETL’s solid oxide fuel cell (SOFC) research.

Research efforts focus primarily on:

(1) The development, refinement, and calibration of models that accurately represent relevant physical and chemical processes (including multiphase flow, electrochemistry, heat transport, chemical reactions and porous media transport phenomena) within a large area fuel cell and (2) the utilization of those models to optimize the composition, microstructure, and operating conditions of different fuel cell systems to design high performing, long lasting, large area fuel cells. Of particular interest is designing high performance cells within fuel cell stacks that are integrated into the electrical grid and may thus operate under variable loading conditions. The models developed and utilized by this researcher must be modified to operate on different client’s workstations, as well as on NETL’s Joule 2.0 supercomputer. This position will also provide support to apply user interfaces on models to facilitate broader accessibility, to integrate the developed models within other SOFC stack/system models, and to communicate the lessons learned from the simulated with external industrial, academic, and national laboratory partners.

Required Qualifications:

The candidate will have a relevant PhD degree and 0-2 years previous experience demonstrating expertise in the following areas:

  • Modeling electrochemical systems from the microstructural level up to the systems level, including interpreting and coding electrochemical and chemical reactions, modeling fluid dynamics and mass/charge transport at the micron level in porous materials, and scale bridging to pass simulation data effectively across multiple length scales. At the individual fuel cell level, experience is required with the calibration, validation, and utilization of multiphysics performance models. At the stack and module level, applicant should be capable of additional modeling thermal management and stress analysis of cell components.
  • Numerical modeling, including ordinary differential equations and partial differential equations. Knowledge and experience with stiff equations solvers using both implicit and explicit modeling schemes using finite volume and finite element discretization methods. Experience with numerous modeling tools including open source software (such as OpenFOAM and MOOSE) and commercial software (such as ANSYS FLUENT and COMSOL Multiphysics).
  • Geometry meshing techniques and meshing software such as ICEM CFD. Proficient in utilizing post-processing tools such as Tecplot and Paraview and using other methods such as MATLAB scripts for more complex post-processing demands.
  • Programming skills:  Proficient in FORTRAN and Matlab. Familiarity preferred in C++, Python, LUA and Visual Basic.
  • Submitting jobs and managing simulations and data within a supercomputing facility using Slurm, including parallelization of tasks. 
  • Collaborating with experimentalists to request experimental data most useful to model development and to guide experimental conditions to gain a better understanding of the underlying physics and chemistry determining overall cell performance.

Preferred Qualifications:

  • With respect to solid oxide fuel cells, the preferred candidate should have a deep understanding of factors impacting long-term fuel cell performance, such as performance limitations in cell components and common degradation phenomena in SOFCs.
  • The preferred candidate will have more than two years postdoctoral experience and have experience in modeling of solid oxide fuel cells in a collaborative work environment such as the National Energy Technology Laboratory.

External Referral Bonus:

Ineligible

Potential for Telework:

No

Clearance Level Required:

None

Travel:

No

Scheduled Weekly Hours:

40

Shift:

Day

Requisition Category:

Professional

Job Family:

Research Scientist

1000

Civil

Leidos is a Fortune 500® information technology, engineering, and science solutions and services leader working to solve the world's toughest challenges in the defense, intelligence, homeland security, civil, and health markets. The company's 33,000 employees support vital missions for government and commercial customers. Headquartered in Reston, Virginia, Leidos reported annual revenues of approximately $10.19 billion for the fiscal year ended December 28, 2018. For more information, visit www.Leidos.com.

Pay and benefits are fundamental to any career decision. That's why we craft compensation packages that reflect the importance of the work we do for our customers. Employment benefits include competitive compensation, Health and Wellness programs, Income Protection, Paid Leave and Retirement. More details are available here.

Leidos will never ask you to provide payment-related information at any part of the employment application process. And Leidos will communicate with you only through emails that are sent from a Leidos.com email address. If you receive an email purporting to be from Leidos that asks for payment-related information or any other personal information, please report the email to spam.leidos@leidos.com.

All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, or any other basis prohibited by law. Leidos will also consider for employment qualified applicants with criminal histories consistent with relevant laws.

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