Intern Computational Materials Science Jobs in Raleigh, NC

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.  

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.