Computational Materials Scientist

Johns Hopkins Applied Physics Laboratory•Laurel, MD

5h•Onsite

About The Position

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.

Requirements

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 verbal and written communication skills.
Willingness and ability to travel occasionally to attend meetings or tests at other government and contractor sites.
Ability to work in closed area facilities.
Ability to obtain a Secret level security clearance. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information. Eligibility requirements include U.S. citizenship.

Nice To Haves

Experience in applying and/or developing machine learning models in conjunction with physics-based simulations to solve problems involving design or selection of inorganic materials.
Experience developing models of inorganic materials, such as metal alloys or ceramic materials, that convey information across models at different length/time scales or physics.
Experience modeling chemical reactions, reaction pathways, thermodynamics, or kinetics using atomistic simulation methods.
Experience modeling inorganic materials and their environmental interactions for specific applications in aerospace, sensing, or energy storage and conversion, corrosion, catalysis, or extreme environments.
Experience applying large language models and tool-enabled AI agents to scientific reasoning tasks including automated hypothesis generation, formal reasoning, and workflow automation.
Experience in algorithm development for accelerating materials science or chemistry calculations on noisy, intermediate-scale quantum (NISQ) devices.

Responsibilities

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.