ZipRecruiter

Log In

1

Phase Field Simulation Jobs (NOW HIRING)

CSA Global LLC

Test stations and system configuration as part of the exercise preparation phase. * Perform ... Master's degree in a related field and 3 years related experience; OR Bachelor's degree in a ...

PPG Industries, Inc.

Modeling Engineer

Springdale, PA · On-site

Explore latest numerical methods (e.g., cohesive zone modeling, phase-field fracture, multi-scale modeling) to expand simulation capabilities. * Data-Driven & Hybrid Modeling: Integrate data ...

PPG

Modeling Engineer

Springdale, PA

Explore latest numerical methods (e.g., cohesive zone modeling, phase-field fracture, multi-scale modeling) to expand simulation capabilities. * Data-Driven & Hybrid Modeling: Integrate data ...

next page

Showing results 1-20

All JobsPhase Field Simulation Jobs

Phase Field Simulation information

What is the difference between Phase Field Simulation vs Materials Scientist?

AspectPhase Field SimulationMaterials Scientist
CredentialsTypically requires a PhD in materials science, physics, or engineering with experience in computational modelingUsually holds a PhD or Master's in materials science, chemistry, or related fields, with research experience
Work EnvironmentPrimarily research labs, simulation software development, computational modelingResearch labs, industry R&D, academia, often combining experiments and analysis
Industry UsageUsed in materials design, phase transformation studies, and computational materials scienceApplied in developing new materials, failure analysis, and material characterization

While both roles involve materials science, Phase Field Simulation focuses on computational modeling of microstructural evolution, whereas Materials Scientists conduct experimental and theoretical research to develop and analyze materials. They often collaborate but serve different functions within the materials industry.

What are the key skills and qualifications needed to thrive as a Phase Field Simulation Engineer, and why are they important?

To thrive as a Phase Field Simulation Engineer, you need a strong background in materials science, computational physics, and mathematical modeling, often supported by an advanced degree in a related field. Familiarity with simulation software like COMSOL Multiphysics, MATLAB, and programming languages such as Python or C++ is typically required. Analytical thinking, attention to detail, and effective communication are valuable soft skills for interpreting results and collaborating with multidisciplinary teams. These skills and qualities are crucial for developing accurate models, solving complex materials problems, and advancing research or industrial applications.

What is phase field simulation?

Phase field simulation is a computational technique used to model the evolution of microstructures in materials, such as solidification, phase transformations, or crack propagation. It involves solving mathematical equations that describe how different phases or structures evolve over time under various physical conditions. This method is widely used in materials science and engineering because it can capture complex interface dynamics without explicitly tracking the boundaries between phases. Researchers use phase field simulations to predict material behavior, optimize processes, and design new materials with desired properties.

What are some common challenges faced by professionals working in phase field simulation roles?

Professionals in phase field simulation often encounter challenges such as managing large-scale computational models, ensuring simulation accuracy, and interpreting complex results. Collaborating closely with interdisciplinary teams—including materials scientists, engineers, and software developers—is essential to translate simulation outcomes into practical applications. Staying updated with the latest advances in computational methods and hardware can also be demanding, but it is key for delivering efficient and innovative solutions in this field.
Infographic showing various Phase Field Simulation job openings in the United States as of June 2026, with employment types broken down into 25% Full Time, and 75% Part Time. Highlights an 90% Physical, 3% Hybrid, and 7% Remote job distribution.
Information Technology_USA - USA_Engineer

Information Technology_USA - USA_Engineer

Real Soft, Inc.

Jacksonville, FL • On-site

Contractor

Posted 12 days ago

Job description

**Please strictly adhere to the following resume naming convention:
ALL CAPS, NO SPACES B/T UNDERSCORES
PTN_US_GBAMSREQID_CandidateBeelineID
i.e. PTN_US_9999999_SKIPJOHNSON0413
MSP Owner: Deepa Narayanan
Location: Santa Clara CA
Duration: 6 Months
Gbams Number: 10680928
ONSITE ROLE
Local candidates preferred.
Ai Hardware Design Engineer
***NOTE: Experience for SciML R&D and exposure in Neural operators, PINNs etc. is required***
We are seeking an Ai Hardware Design Engineer to join our team and drive innovation in AI-powered solutions. This role involves designing, developing, and optimizing generative AI models and workflows for applications such as content creation, product design, and intelligent automation.
• Develop forward surrogate models for CVD/ALD/etch chambers mapping geometry, gas chemistry, flow, temperature, and power to film-uniformity, step-coverage, particle behavior, and thermal outcomes.
• Implement inverse-design workflows where target performance specifications generate feasible chamber geometries, showerhead/baffle designs, and process conditions via generative or adjoint/topology-optimization methods.
• Build bi-directional models that infer optimal process parameters for a given geometry and recommend geometry modifications when process latitude is insufficient.
• Create high-fidelity digital twins combining physics-based solvers (CFD, plasma, heat transfer) with learned surrogate components for rapid design-space exploration.
• Platform & MLOps Infrastructure: Implement and maintain robust, containerized MLOps systems (Docker, Kubernetes) in HPC environments to deploy models efficiently.
• Develop robust multi-objective optimization and uncertainty-quantification workflows to ensure AI-generated designs are manufacturable, robust to variation, and compatible with downstream yield requirements.
• Collaborate with physicists, domain experts, and software engineers to validate that AI models comply with fundamental scientific laws.
•
Required Skills & Qualifications
• Education: Master's or Ph.D. in Computer Science, Computational/Electrical Engineering, AI/ML, or related field.
• Technical Expertise:
o Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow).
o Experience with generative AI (LLMs, diffusion models, graph-based models).
o Knowledge of computational materials methods (DFT, MD, phase-field modeling).
• Additional Skills:
o Familiarity with MLOps, HPC environments, and cloud deployment.
o Proven experience (code repos, publications) bridging simulation software, hardware design, and ML.
Skills: Digital : Python~Digital : Machine Learning
Experience Required: 6-8, Project Code :

Apply