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Computational Materials Scientist

Job in Laurel, Anne Arundel County, Maryland, 20724, USA
Listing for: The Johns Hopkins University Applied Physics Laboratory
Full Time position
Listed on 2026-09-27
Job specializations:
  • Research/Development
    Research Scientist
  • Engineering
    Research Scientist
Salary/Wage Range or Industry Benchmark: 105000 - 290000 USD Yearly USD 105000.00 290000.00 YEAR
Job Description & How to Apply Below
Location: Laurel

Description

Are you a creative person driven to solve new problems?

Are you searching for impactful work in computational engineering and research that doesn't confine you to working on the same thing year after year?

Does using atomistic and multiscale modeling to understand, design, and optimize advanced alloys and ceramics for aerospace, sensing, or energy-storage applications sound like a dream job?

If so, we're looking for someone like you to join our team at APL.

We are seeking a Computational Materials Scientist to develop and apply atomistic and multiscale computational methods to understand and predict the behavior of inorganic materials, particularly advanced metal alloys and ceramics. You will model key physical processes, from atomic-scale structure, defects, and chemical interactions to effective material properties, to solve impactful challenges in aerospace, sensing, energy storage, and other applications. As a member of our team, you will contribute to exciting projects supporting the US Department of War and other government agencies.

Our team strives to develop, apply, and maintain deep expertise in multiscale modeling techniques that give insight across key length and time scales. You will work alongside analysts, laboratory scientists, and engineers who have a passion for applying our modeling results to physical systems that advance the state of the art and have real-world impact.

As a Computational Materials Scientist, you will:
  • Develop and use models of metals and ceramics to determine relationships between structure and function across a variety of length and time scales, from atomic-scale lattice structure and defects through phase and microstructure evolution to effective engineering properties.
  • Leverage modeling methods including classical molecular dynamics, electronic structure, reaction pathway and kinetic modeling, coarse graining, enhanced sampling, statistics, and machine learning models.
  • Design and apply scalable computational workflows to accelerate materials discovery and optimization through high-throughput simulation, data-driven analysis, and physics-based modeling.
  • Quantify uncertainty, validate predictions against experimental data, and assess model applicability across relevant materials and operating conditions.
  • Actively collaborate with analysts, scientists, and engineers on a day-to-day basis to guide materials discovery and interpret experimental observations.
  • Propose future projects and initiatives.
  • Craft reports and give presentations to communicate results to team members and government partners.

We're looking for talented and versatile computational researchers who are excited to expand their analytical toolbox. If you have experience in any of the methods or tools above, and are motivated to learn even more, we want to talk to you.

Qualifications

You meet our minimum qualifications for the job if you...

  • A Ph.D. in Materials Science, Chemistry, Mechanical Engineering, Chemical Engineering, Physics, Applied Mathematics, or equivalent with demonstrated application of knowledge to answer complex questions.
  • 3+ years of experience in performing physics-based simulations of fundamental properties in inorganic solid materials, such as electronic structure calculations or classical/ab initio molecular dynamics.
  • Scientific/engineering programming experience, with the ability to work in multiple languages (MATLAB, C/C++, Python, FORTRAN, ...) and with algorithms commonly used in computational science and engineering.
  • Demonstrated ability to work both independently and collaboratively within a multidisciplinary team and incorporate multiple experimental data types into computational workflows
  • Ability to manage and prioritize across multiple projects.
  • Excellent…
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