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Computational Phase-Field Modeler

Company: Leidos
Location: Albany
Posted on: June 27, 2019

Job Description:

Job Description:

No two career paths will ever look the same. At Leidos, we know the most talented and diverse IT and engineering professionals will always have a multitude of career choices; your time at Leidos will be a wise investment in your career and in yourself. We welcome your perspective and ideas, in order to foster collaboration and deliver world-class solutions. We look for solutions that not only transform businesses, but change the world.

Our Civil business is helping to modernize and manage infrastructure, systems and controls, and cybersecurity for civilian agencies and commercial clients around the globe. With core competencies in information technology, energy and environment, complex logistics, and specialized engineering, we solve technical challenges and implement newfound efficiencies on a number of programs!

Protect yourself and your family, with the benefits of working for a world-class employer. When you join Leidos, you join a Fortune 500 company and one of Ethisphere Institute's "World's Most Ethical Companies"

Leidos...We strive to make the complex clear

An opportunity exists for a PhD - level computational scientist to develop and utilize computational models and data analysis tools to support NETL's solid oxide fuel cell (SOFC) research. For this position, all models are being developed using a phase field approach.

Research efforts focus primarily on the following tasks:

  • 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) that determine overall performance within a fuel cell.
  • Development, refinement, and calibration of degradation models that accurately represent any relevant physical, chemical, and electrochemical processes that modify the overall performance of a fuel cell during operation under relevant operating conditions. For this position, particular interest is given to particle coarsening and the formation of secondary phases at solid-solid and gas-solid interfaces.
  • Combination of the performance and degradation models into a single framework to allow for large-scale parametric studies to minimize long-term performance degradation for a given SOFC system by adjusting the composition, structure, and/or operating conditions of the cell.
  • Collaboration with experimentalists to define the experiments necessary for proper calibration and validation of the developed models and to provide optimized fuel cell compositions and structures that can be tested experimentally.

    Required Qualifications:
    • The candidate will have a relevant PhD degree and 2 years prior relevant experience; will possess significant experience in identified computer programming (C++, FORTRAN, MATLAB, Python) and will have experience in programming simulation jobs within a high-performance computing environment (e.g. parallel processing and programming in a message passing interface (MPI) environment).
    • The candidate should possess excellent communication skills and have experience 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.

      Additionally, the candidate will have a strong background in the following areas:
      • Numerical modeling, including ordinary differential equations and partial differential equations, and the use of stiff equation solvers with both implicit and explicit modeling schemes for partial differential equations.
      • Phase field models for microstructure evolution modeling, including coupling multiple fields and multiple diffusion mechanisms in the diffusive-interface framework.
      • Modeling electrochemical systems on the micron-to-millimeter scale, including interpreting and coding electrochemical and chemical reactions, modeling fluid dynamics and mass/charge transport in porous materials, and scale bridging to pass simulation data effectively across multiple length scales.
      • Familiarity with submitting jobs and managing simulations and data within a supercomputing facility using Slurm, including parallelization of tasks when possible.

        Preferred Qualifications:
        • Atomistic scale simulation, such as density functional theory, molecular dynamics and kinetic Monte Carlo, to calculate theoretical energetic and kinetic parameters for phase field model inputs.
        • Machine learning techniques such as neural networks and Gaussian processes, as well as Monte Carlo simulation for Bayesian calibrations, used in the development of reduced order models for scale-bridging efforts and high throughput computational studies.
        • Generating synthetic 3D microstructures with specific microstructural parameters using the software DREAM.3D.
        • Utilizing software such as Tecplot and Paraview for visualization and analysis of 3D data.
        • 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.

Keywords: Leidos, Aloha , Computational Phase-Field Modeler, Other , Albany, Oregon

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