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Numerical Modeling Multiscale Jobs (NOW HIRING)

Ucf

Post Doctoral Scholar

Orlando, FL

Experience in development and validation of multiscale numerical modeling techniques. Special Instructions to the Applicants: Applications should include: (i) a cover letter, (ii) detailed Curriculum ...

Micron

Senior Engineer, Technology - AMAIS

Boise, ID

$99K - $136K/yr

Advanced multiscale structural modeling expertise. * Strong foundation in numerical methods, high-performance computing (HPC), and ML/AI algorithms. * Experience with innovative multiphysics and ...

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How much do numerical modeling multiscale jobs pay per hour?

As of Jun 4, 2026, the average hourly pay for numerical modeling multiscale in the United States is $23.03, according to ZipRecruiter salary data. Most workers in this role earn between $19.23 and $25.96 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Numerical Modeling Multiscale specialist, and why are they important?

To excel as a Numerical Modeling Multiscale specialist, you need a strong background in mathematics, physics, and computational science, often supported by an advanced degree in engineering or applied sciences. Expertise in simulation software (such as ANSYS, COMSOL, or MATLAB), programming languages (like Python or C++), and familiarity with high-performance computing systems are typically required. Analytical thinking, attention to detail, and effective communication skills are essential soft skills in this field. These abilities enable accurate simulations, clear interpretation of complex data, and collaborative problem-solving, which are vital for advancing research and developing innovative solutions.

What are some common challenges faced by Numerical Modeling Multiscale specialists when integrating models across different spatial and temporal scales?

Numerical Modeling Multiscale specialists often encounter challenges when coupling models that operate at different spatial and temporal resolutions. Ensuring data consistency and minimizing numerical errors at the interfaces between scales can be complex, often requiring advanced interpolation techniques and careful validation. Additionally, balancing computational efficiency with model accuracy is a frequent concern, as multiscale models can be resource-intensive. Effective collaboration with domain experts, such as physicists or engineers, is often essential to refine model parameters and interpret results accurately.

What is numerical modeling multiscale?

Numerical modeling multiscale refers to the use of computational methods to simulate and analyze systems that operate across multiple spatial or temporal scales. This approach allows researchers to study complex phenomena by linking models that describe small-scale processes, such as molecular interactions, with models of larger-scale behavior, like fluid flow or material deformation. Multiscale modeling is widely used in fields like materials science, biology, and engineering to gain insights that would be difficult or impossible to obtain through experiments alone. These models require sophisticated algorithms and significant computational resources to accurately represent interactions between different scales.

What is the difference between Numerical Modeling Multiscale vs Numerical Simulation Engineer?

AspectNumerical Modeling MultiscaleNumerical Simulation Engineer
CredentialsTypically requires advanced degrees in engineering or applied mathematicsRequires engineering or physics degrees, often with specialization in simulation
Work EnvironmentResearch labs, academia, or industry focusing on complex systemsEngineering firms, manufacturing, or software companies
Industry UsageUsed for multiscale problems across materials, fluids, and biological systemsApplied for detailed simulations of specific engineering components or processes

Numerical Modeling Multiscale focuses on developing models that bridge multiple scales in complex systems, while Numerical Simulation Engineer applies these and other simulation techniques to solve specific engineering problems. Both roles require strong analytical skills and familiarity with computational tools, but their scope and application differ.

Research Scientist I/II, Multiscale & Multiphysics Simulations

Lila Sciences

Cambridge, MA • On-site

Other

Posted 20 days ago

Job description

Your Impact at LILA

Your role will focus on building next-generation in silico multiphysics and multiscale simulation capabilities that power AI-driven scientific discovery. You will develop high-fidelity digital representations of complex physical systems spanning chemical and mechanical processes, transport phenomena, and electromagnetic behavior and integrate them into autonomous discovery and experimental pipelines.

You will work on integrating simulation methods-such as finite element modeling, computational fluid dynamics, phase-field methods, and TCAD-style transport/process modeling-into scalable, programmatic, and agent-driven systems that enable real-time digital twins, simulation-informed decision-making, and autonomous closed-loop workflows

What You'll Be Building

  • Develop and deploy robust multiphysics models across coupled domains (e.g., thermal, fluid, structural, electromagnetic, chemical), using methods such as coarse-grained, mesoscale, FEM, and CFD techniques.
  • Build integrated multiscale frameworks that connect atomistic, mesoscale, and continuum representations to model materials and devices.
  • Design and implement programmatic, agent-driven simulation workflows that can autonomously configure, execute, and refine simulations within closed-loop discovery workflows.
  • Create scalable, GPU-accelerated simulation pipelines, data infrastructure, and interoperable APIs that connect commercial tools (e.g., COMSOL, ANSYS) and custom solvers deploying on cloud-based, high-throughput computing environments
  • Collaborate with AI, software, and automation teams to orchestrate and deploy closed-loop discovery workflows, integrating computational predictions with robotic and cloud-based laboratory platforms to enable automated experiment-simulation feedback cycles and accelerated R&D.

What You'll Need to Succeed

  • PhD in Mechanical Engineering, Chemical Engineering, Aerospace Engineering, Materials Science, or a related field.
  • Extensive experience with multiphysics simulation methods and numerical algorithms, including FEM, CFD, TCAD/process simulation, mesoscale modelling, or related techniques.
  • Strong foundation in coupled physical phenomena, including heat transfer, fluid dynamics, structural mechanics, mass transport, diffusion, electromagnetism, and reaction kinetics.
  • Experience applying simulation to real-world systems in industrial settings such as semiconductors, chemical processing, aerospace, or materials manufacturing.
  • Solid programming skills in Python and building simulation workflows, automation pipelines, or custom numerical models.

Bonus Points For

  • Experience bridging atomistic simulations with one or more additional simulation domains including coarse-grained, finite-element and continuum models.
  • Familiarity with machine learning approaches applied to physical simulations (e.g., surrogate models, neural operators, physics-informed neural networks), along with experience leveraging GPU acceleration and programmatic optimization for scalable simulations
  • Experience integrating simulation frameworks into digital twin systems, real-time decision environments, or closed-loop control workflows.
  • Background applying simulation to complex materials and process domains such as thin-film deposition, micro/nano-fabrication, or reactive transport, with an understanding of processing-structure-property relationships.

Apply