Computational Materials Scientist: Alloys & Ceramics

Johns Hopkins Applied Physics Lab; Laurel (MD); On-site; USD 105,000 - 290,000; Full time

Job summary

Johns Hopkins Applied Physics Laboratory in Laurel, MD, seeks a Computational Materials Scientist to develop atomistic and multiscale models for metals and ceramics used in aerospace, sensing, and energy storage.

You will design scalable workflows, quantify uncertainty, and collaborate with analysts and engineers to translate modeling into real-world decisions for government partners. A Ph.D. and relevant experience are required, with US citizenship and security clearance likely.

Qualifications

• Ph.D. in Materials Science, Chemistry, Mechanical or Chemical Engineering, Physics, Applied Mathematics, or equivalent.
• 3+ years of physics-based simulations of inorganic solids (electronic structure or classical/ab initio MD).
• Proficiency in multiple programming languages (MATLAB, C/C++, Python, FORTRAN).
• Ability to work independently and in cross-disciplinary teams; manage multiple projects.

Responsibilities

• Develop and apply atomistic and multiscale models for metals and ceramics.
• Leverage MD, electronic structure, and kinetic modeling methods.
• Design scalable computational workflows for high-throughput materials discovery.
• Quantify uncertainty and validate predictions with experimental data.
• Collaborate with analysts, scientists, and engineers to interpret results.
• Propose future projects and communicate findings through reports and presentations.

Skills

MATLAB

C/C++

Python

FORTRAN

Machine learning

Education

Ph.D. in Materials Science or related field

Tools

Molecular dynamics

Electronic structure

Kinetic modeling

Coarse graining

High-throughput workflows

Job description

Johns Hopkins Applied Physics Laboratory in Laurel, MD, seeks a Computational Materials Scientist to develop atomistic and multiscale models for metals and ceramics used in aerospace, sensing, and energy storage.

You will design scalable workflows, quantify uncertainty, and collaborate with analysts and engineers to translate modeling into real-world decisions for government partners. A Ph.D. and relevant experience are required, with US citizenship and security clearance likely.