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Intern Computational Materials Science Jobs in Raleigh, NC

RTNN Intern

Raleigh, NC

$13 - $17.50/hr

... Materials Science & Engineering System Information Classification Title Temporary-Professional NonFaculty Working Title RTNN Intern

... Materials Science and Engineering About the Department The Department of Materials Science and ... computational materials. Wolfpack Perks and Benefits As a Pack member, you belong here, and can ...

As a Manger of Research Science in Machine Learning you will direct the activities of a research ... Expertise in computational modeling of materials systems such as DFT and finite element model ...

As a Manger of Research Science in Machine Learning you will direct the activities of a research ... Expertise in computational modeling of materials systems such as DFT and finite element model ...

Strong interest in community engagement, data science, or social impact * Experience with data ... Designing materials for print and digital collateral * Maintaining accurate data records and ...

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Intern Computational Materials Science information

What is the difference between Intern Computational Materials Science vs Intern Materials Engineering?

AspectIntern Computational Materials ScienceIntern Materials Engineering
Required CredentialsUndergraduate or graduate in materials science, physics, or related fields; basic programming skillsUndergraduate or graduate in materials engineering, mechanical engineering, or related fields; foundational technical knowledge
Work EnvironmentResearch labs, computational modeling, data analysisDesign, testing, and development in labs or manufacturing settings
Industry UsageResearch institutions, tech companies, aerospace, academiaManufacturing firms, product development, construction

Intern Computational Materials Science focuses on computational modeling and simulations of materials properties, while Intern Materials Engineering emphasizes practical design, testing, and application of materials. Both roles require a background in materials-related fields but differ in their core activities and work environments.

What does an Intern in Computational Materials Science do?

An Intern in Computational Materials Science assists in research and development by applying computational techniques to study and predict the properties and behaviors of materials. Typical tasks include running simulations, analyzing data, and working with software tools to model materials at the atomic or molecular level. Interns may collaborate with researchers to design experiments, interpret results, and contribute to scientific publications or reports. This role provides hands-on experience in both computational methods and materials science, helping to bridge theory and practical application.

What are the key skills and qualifications needed to thrive as an Intern in Computational Materials Science, and why are they important?

To thrive as an Intern in Computational Materials Science, you need a solid background in materials science, physics, or engineering, along with coursework in computational modeling and data analysis. Familiarity with programming languages like Python or MATLAB, experience with simulation software (such as VASP or LAMMPS), and knowledge of high-performance computing are typically required. Strong analytical thinking, attention to detail, and effective teamwork are important soft skills for success in collaborative research environments. These skills enable interns to contribute meaningfully to research projects, analyze complex materials data, and communicate findings clearly within multidisciplinary teams.

What types of projects can an Intern in Computational Materials Science expect to work on during their internship?

As an Intern in Computational Materials Science, you can expect to engage in projects involving simulations of material properties, data analysis from computational experiments, and the development of models to predict material behavior. You may collaborate with researchers and senior scientists to support ongoing investigations or help optimize simulation workflows. These projects often require proficiency in programming languages such as Python or MATLAB and may involve the use of specialized software like VASP or LAMMPS. The experience provides a hands-on understanding of how computational methods contribute to advancing materials research and often includes opportunities to present your findings to the team.
What are popular job titles related to Intern Computational Materials Science jobs in Raleigh, NC? For Intern Computational Materials Science jobs in Raleigh, NC, the most frequently searched job titles are:
What job categories do people searching Intern Computational Materials Science jobs in Raleigh, NC look for? The top searched job categories for Intern Computational Materials Science jobs in Raleigh, NC are:
What cities near Raleigh, NC are hiring for Intern Computational Materials Science jobs? Cities near Raleigh, NC with the most Intern Computational Materials Science job openings:
Infographic showing various Intern Computational Materials Science job openings in Raleigh, NC as of July 2026, with employment types broken down into 71% Full Time, 28% Part Time, and 1% Contract. Highlights an 71% Physical, 2% Hybrid, and 27% Remote job distribution.

Modeling and Simulation Manager

Vulcan Elements

Raleigh, NC

Full-time

Re-posted 8 hours ago


Job description

Vulcan Elements is manufacturing American rare-earth permanent magnets for a secure, resilient future. With a focus on national security and economic resiliency, we serve critical industries such as defense, aerospace, and automotive powering a high-technology future. Vulcan Elements is building a team of ambitious professionals committed to Mission Focus, Technical Excellence and Transparency.

As the Modeling and Simulation Manager you will grow and lead a multidisciplinary team of computational scientists and engineers, driving physics-based and data-based modeling efforts that directly improve magnet performance, production yields, and process efficiency.

This is a hands-on technical leadership role. You will both contribute directly to modeling work and grow a team with deep expertise across a variety of modeling and simulation modalities, including but not limited to mechanical, fluid dynamics, electronic structure, and mathematical and image analysis. Your group's output will shape process decisions, reduce experimental iteration cycles, and create quantitative links between structure, processing conditions, and final properties.

This role will initially support pilot-scale and R&D activities (RTP, NC), with the potential to contribute to commercial plant operations (Benson, NC) in the next year.

The ideal candidate has hands-on experience with one or more of COMSOL, Fluent, finite element analysis, thermodynamic modeling (FactSage, ThermoCalc), mathematical and image analyses, and density functional theorgy, as well as experience as a people leader.

Responsibilities

Build and manage a matrixed modeling and simulations team with broad expertise spanning Finite element analysis, Computational fluid dynamics, Stress/Strain analysis, thermodynamic modeling, mathematical/image analysis, and DFT; define hiring roadmap, team structure, and capability milestones.

  • Translate R&D priorities into a modeling agenda with clear deliverables, timelines, and success criteria in partnership with process engineering, materials science, and production teams.
  • Foster a culture of scientific rigor, reproducibility, and rapid iteration; establish standards for model validation, documentation, and peer review.
  • Present results and recommendations to technical and executive stakeholders; communicate uncertainty and model limitations clearly.

Responsibilities and tasks outlined are not exhaustive and may change as determined by the needs of the business.

Qualifications

  • Ph.D. or M.S. in Materials Science, Chemical Engineering, Mechanical Engineering, Applied Physics, or a related quantitative field.
  • 5+ years of hands-on experience with COMSOL, Fluent, or equivalent; demonstrated ability to build and validate coupled multiphysics models and finite element analysis (FEA).
  • Strong mathematical modeling background; experience with image analysis, machine learning, statistical modeling, or signal processing applied to materials characterization data.
  • Familiarity with rare earth magnets, NdFeB alloy systems, or related metallic/magnetic materials.
  • Experience leading or mentoring a small technical team; ability to set priorities, provide technical guidance, and develop junior researchers.
  • Proficiency in Python, MATLAB, Git, or equivalents for scripting, data analysis, and model post-processing.
  • Strong written and verbal communication skills; able to produce clear technical reports and present complex results to non-specialist audiences.

Must be a U.S. Person due to required access to U.S. export-controlled information or facilities

Preferred Qualifications

  • Direct experience with DFT codes (VASP, Quantum ESPRESSO, or similar) or demonstrated ability to direct and interpret DFT studies.
  • Background in rare earth processing, hydrometallurgy, or magnet manufacturing.
  • Experience with grain boundary diffusion processes or sintering simulation.
  • Familiarity with thermodynamic modeling and databases such as FactSage, or ThermoCalc
  • Knowledge of machine learning principles for data analysis
  • Familiarity with electron microscopy image analysis (SEM/EDS/EBSD) and quantitative microstructural characterization.
  • Publication record in computational materials science, process simulation, or permanent magnets.
  • Experience managing budgets, external collaborators, or academic partnerships.