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Computational Materials Science Jobs (NOW HIRING)

Novelis

Lead Modeling Scientist

Kennesaw, GA · On-site

D.) in Materials Science, Mechanical Engineering, or a related field, with more than ten years of experience in computational modeling. * Expertise in metallurgy and materials science, as well as ...

Worcester Polytech

Post-Doctoral Fellow

Worcester, MA · On-site

$45K - $70K/yr

... computational materials science with a focus on advanced density functional theory (DFT), machine learning, and multiscale materials modeling for alloys and ceramics. The successful candidate will ...

The Pennsylvania State University

Work alongside experts in materials science, metallurgy, engineering and performance modeling to ... Working knowledge of computational and analytical corrosion research methods * Comfortable working ...

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Computational Materials Science information

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$142.5K

$168.8K

$192.5K

How much do computational materials science jobs pay per year?

As of Jun 12, 2026, the average yearly pay for computational materials science in the United States is $168,844.00, according to ZipRecruiter salary data. Most workers in this role earn between $155,500.00 and $182,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Computational Materials Scientist, and why are they important?

To thrive as a Computational Materials Scientist, you need a solid background in materials science, physics, or chemistry, often with a graduate degree and experience in scientific computing. Proficiency with simulation software (such as VASP, LAMMPS, or Quantum ESPRESSO), programming languages (like Python, C++, or Fortran), and familiarity with high-performance computing systems is typically required. Critical thinking, problem-solving abilities, and effective collaboration and communication skills set outstanding candidates apart. These competencies are crucial for designing, executing, and interpreting complex simulations and for translating computational insights into real-world materials innovations.

What is the difference between Computational Materials Science vs Materials Engineer?

AspectComputational Materials ScienceMaterials Engineer
Required CredentialsTypically requires a PhD or Master's in materials science, physics, or chemistryBachelor's or Master's in materials engineering or related field
Work EnvironmentResearch labs, universities, or R&D departments focusing on simulations and modelingManufacturing plants, design offices, or product development teams
Industry UsagePrimarily in research, academia, and advanced R&D projectsProduction, quality control, and product development in manufacturing industries
Common Search/ComparisonYesYes

Computational Materials Science focuses on using computer simulations and modeling to understand and predict material behavior, often requiring advanced degrees. Materials Engineers work on designing, testing, and improving materials in practical applications, usually with a bachelor's or master's degree. While both roles are integral to materials development, Computational Materials Science is more research-oriented, whereas Materials Engineering emphasizes application and production.

What are some common challenges faced by professionals in Computational Materials Science, and how can they be addressed?

Professionals in Computational Materials Science often encounter challenges such as dealing with large datasets, managing the complexity of multi-scale simulations, and ensuring the accuracy of computational models. Addressing these challenges typically involves staying updated on the latest simulation software, collaborating closely with experimental teams to validate results, and developing strong programming and data analysis skills. Effective communication and interdisciplinary teamwork are also key, as projects often require input from chemists, physicists, and engineers to achieve successful outcomes.

What Are Computational Materials Science Jobs?

Jobs in computational materials science include academic and research positions in university settings. You can also find positions in the manufacturing industry. As a research scientist in computational materials science, your duties are to develop hypotheses and test them using computational modeling software and a variety of investigatory tools, such as Monte Carlo algorithms, density function theory, phase field models, and finite element methods. Your responsibilities include gathering data, testing modeling software, collaborating with other researchers to develop tools that aid them in their research, and analyzing data to write reports, journal articles, or presentations for conferences.

What is computational materials science?

Computational materials science is a field that uses computer-based simulations and modeling to understand, predict, and design the properties and behaviors of materials. Researchers use mathematical models, algorithms, and high-performance computing to study materials at the atomic, molecular, or macroscopic level. This approach allows scientists to accelerate the discovery of new materials, optimize existing ones, and investigate phenomena that may be difficult or expensive to study experimentally.
More about Computational Materials Science jobs
What cities are hiring for Computational Materials Science jobs? Cities with the most Computational Materials Science job openings:
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Computational Materials Science jobs near you
Infographic showing various Computational Materials Science job openings in the United States as of June 2026, with employment types broken down into 86% Full Time, and 14% Part Time. Highlights an 86% In-person, and 14% Hybrid job distribution, with an average salary of $168,844 per year, or $81.2 per hour.
Postdoctoral Appointee - Experimental AI

Postdoctoral Appointee - Experimental AI

Argonne National Laboratory

Lemont, IL • On-site

$49K - $67K/yr

Full-time

Posted 21 days ago

Job description

Job Summary:
Argonne National Laboratory is dedicated to scientific discovery and innovation, and they are seeking a Postdoctoral Appointee for research in experimental AI applications. The role involves computational and experimental work aimed at developing AI and automation tools for electrochemical research.
Responsibilities:
• developing and implementing experimental focused application of AI and automation tools for unraveling fundamental interfacial processes in materials for electrochemistry
• involve some experimental work in adapting workflows for automation and artificial intelligence optimization schemes
• developing AI models to uncover structure-function relationships with limited data sets
• building automated electrode-electrolyte interface discovery workflows
• implementing full autonomous experimental campaigns
• learning experimental workflows and adapting them for autonomous control
Qualifications:
Required:
• Ph.D. completed in the past 5 years or soon-to-be completed in chemistry, chemical engineering, materials science, or a closely related field
• Strong background in fundamental electrochemistry
• Well versed in code development
• Application of AI model classifiers (PLS-DA, random forest, neural network, etc) towards unraveling materials structure-function relationships
• Familiar with optimization approaches such as genetic search, Bayesian optimization
• Ability to think and work independently
• Conduct research in a highly interdisciplinary environment of chemists, physicists, and materials scientists
• Good communication skills and fluent in both spoken and written English
• Demonstrate evidence of initiative and problem-solving in research projects
Preferred:
• Hands-on expertise in computational materials science
• Familiarity with electrochemical instrumentation suite (potentiostat, cyclers, electronic load) and materials characterization
Company:
Argonne National Laboratory conducts researches in basic science, energy resources, and environmental management. Founded in 1946, the company is headquartered in Lemont, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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