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Computational Materials Science Jobs (NOW HIRING)

Entegris, Inc.

$101 - $126/hr

## Senior Scientist, Computational Materials SolutionsApplylocations: Remote, PAtime type: Full ... This role focuses on accelerating innovation by combining computational science, machine learning ...

SES

Computational Materials Scientist

Woburn, MA · On-site +1

$180K - $200K/yr

This powerful combination of "AI for science" and material engineering enables batteries that can ... As a Computational Materials Scientist, you will be a core data-driven modeler responsible for ...

Mat3ra

Computational Materials Scientist

Walnut Creek, CA · On-site +1

$90K - $140K/yr

Overview We look for computational materials scientists excited about bridging the gap between ... science to help us develop a software framework for designing and discovering new advanced ...

Mercor

Materials Scientist

Detroit, MI · On-site

$70 - $110/hr

... science, materials engineering, or related fields. * 4+ years of research or industrial R&D experience in materials. * Specialization in areas like energy storage, semiconductors, or computational ...

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How much do computational materials science jobs pay per year?

As of Aug 25, 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 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.

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 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 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 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.

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What cities are hiring for Computational Materials Science jobs?

Cities with the most Computational Materials Science job openings:

What are the most commonly searched types of Computational Materials Science jobs?

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What states have the most Computational Materials Science jobs?

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computational materials science jobs near you
Infographic showing various Computational Materials Science job openings in the United States as of August 2026, with employment types broken down into 67% Full Time, 13% Part Time, and 20% Contract. Highlights an 93% In-person, and 7% Remote job distribution, with an average salary of $168,844 per year, or $81.2 per hour.

Computational Materials Scientist

Discovery 2 Scale

Santa Fe, NM • On-site

$110 - $150/hr

Other

Medical, Retirement

Posted 7 days ago

Job description

Discovery 2 Scale is building the Autonomous Foundry for Advanced Materials—an end-to-end platform connecting materials design, autonomous experimentation, qualification, and scale-up to deliver production-ready materials faster and with less risk.

We are looking for a highly skilled computational materials scientist with strong CALPHAD and Thermo-Calc experience to help build the thermodynamic intelligence behind our platform.

What you’ll do
  • Develop CALPHAD and Thermo-Calc workflows for alloy design and high-throughput screening.
  • Model phase stability, solidification, segregation, and phase transformations.
  • Perform Scheil and kinetic simulations to evaluate manufacturability and processing windows.
  • Develop models for weldability, heat-affected-zone behavior, and thick-section producibility.
  • Build automated Python and Thermo-Calc pipelines for large composition and processing spaces.
  • Integrate thermodynamic predictions with AI/ML models and experimental data to guide alloy design.
Required qualifications
  • M.S. or Ph.D. in Materials Science, Metallurgy, Computational Materials Science, or a related field.
  • Hands-on experience with Thermo-Calc and CALPHAD.
  • Experience modeling multicomponent metallic alloy systems.
  • Experience with equilibrium phase and Scheil solidification calculations.
  • Strong understanding of physical metallurgy, thermodynamics, solidification, and phase transformations.
  • Experience with scientific programming or workflow automation using Python or similar tools.
  • Ability to connect computational predictions with real materials processing and experimental observations, and work independently in a fast-paced startup environment.
Preferred qualifications
  • Experience with TC-Python, DICTRA, TC-PRISMA, or related thermodynamic and kinetic modeling tools.
  • Experience with steels, nickel-based alloys, titanium or refractory alloys, or multi-principal-element alloys.
  • Experience with welding metallurgy, HAZ transformations, solidification, segregation, or casting.
  • Experience integrating CALPHAD predictions with SEM, EDS, EBSD, or other experimental data.
  • Experience combining CALPHAD with machine learning, materials informatics, or high-throughput alloy design.
Why join D2S?
  • Build cutting-edge autonomous materials discovery technology.
  • Work alongside world-class scientists and engineers.
  • High level of ownership and technical freedom.
  • Opportunity to help build a company from the ground up.
  • Competitive salary, comprehensive health benefits, and a 401(k) with company match.
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