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Computational Modeling Simulation Multiphysics Jobs in Rochester, MI

Cummins

RePower Program - Project Engineering

Troy, MI · On-site

Modeling & Simulation : Influence design decisions through computational modeling, simulation, and analysis that predict system and component performance. * Interface Integration : Ensure seamless ...

Cummins

RePower Program - Project Engineering

Troy, MI · On-site

Modeling & Simulation : Influence design decisions through computational modeling, simulation, and analysis that predict system and component performance. * Interface Integration : Ensure seamless ...

Cummins

RePower Program - Project Engineering

Troy, MI · On-site

Modeling & Simulation : Influence design decisions through computational modeling, simulation, and analysis that predict system and component performance. * Interface Integration : Ensure seamless ...

Cummins

RePower Program - Project Engineering

Troy, MI · On-site

Modeling & Simulation : Influence design decisions through computational modeling, simulation, and analysis that predict system and component performance. * Interface Integration : Ensure seamless ...

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All JobsComputational Modeling Simulation Multiphysics JobsComputational Modeling Simulation Multiphysics Jobs in Rochester, MI

Computational Modeling Simulation Multiphysics information

See Rochester, MI salary details

$35.9K

$93.2K

$132.5K

How much do computational modeling simulation multiphysics jobs pay per year?

As of Jul 9, 2026, the average yearly pay for computational modeling simulation multiphysics in Rochester, MI is $93,201.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,300.00 and $119,200.00 per year, depending on experience, location, and employer.

What is the difference between Computational Modeling Simulation Multiphysics vs Computational Engineer?

AspectComputational Modeling Simulation MultiphysicsComputational Engineer
CredentialsTypically requires degrees in engineering, physics, or related fields; certifications in simulation software are commonSimilar educational background; often holds engineering degrees and software certifications
Work EnvironmentPrimarily in R&D labs, engineering firms, or manufacturing settings focusing on complex simulationsInvolved in product development, software development, or systems design in various industries
Industry UsageUsed in aerospace, automotive, energy, and manufacturing for advanced simulationsApplied across industries for designing, analyzing, and optimizing systems and products

While both roles involve computational skills and engineering principles, Computational Modeling Simulation Multiphysics specializes in complex, multi-physics simulations, whereas Computational Engineer focuses on designing and implementing computational solutions across various engineering projects.

What are the key skills and qualifications needed to thrive as a Computational Modeling Simulation Multiphysics Engineer, and why are they important?

A strong background in physics, engineering, mathematics, and computational science—typically with an advanced degree—is essential for a Computational Modeling Simulation Multiphysics Engineer. Proficiency in simulation software such as ANSYS, COMSOL Multiphysics, MATLAB, and programming languages like Python or C++ is commonly required, along with familiarity with high-performance computing environments. Analytical thinking, problem-solving skills, and effective communication set standout professionals apart in this field. These capabilities enable accurate modeling of complex physical phenomena, efficient collaboration, and successful project outcomes in research and industry settings.

What is computational modeling simulation multiphysics?

Computational modeling simulation multiphysics refers to the use of computer-based models to simulate and analyze systems that involve multiple interacting physical phenomena—such as fluid dynamics, heat transfer, electromagnetics, and structural mechanics—all at once. This approach allows researchers and engineers to predict complex real-world behavior, optimize designs, and reduce the need for expensive prototypes. Multiphysics simulations are widely used in industries like aerospace, automotive, energy, and biomedical engineering, where accurate modeling of coupled physical processes is critical.

What are some common challenges faced by professionals in Computational Modeling Simulation Multiphysics roles, and how can they be addressed?

One of the main challenges in Computational Modeling Simulation Multiphysics roles is managing the complexity of integrating multiple physical phenomena, such as thermal, structural, and fluid dynamics, into a single simulation. This often requires a deep understanding of both the underlying physics and the numerical methods used by simulation software. Collaborating closely with domain experts and maintaining clear communication within multidisciplinary teams can help address these challenges. Additionally, staying updated with advances in simulation tools and best practices through continuous learning is key to overcoming technical hurdles and ensuring accurate results.
Computational Modeling Simulation Multiphysics jobs near you
Senior Optimization Engineer

Senior Optimization Engineer

Optimal Inc.

Warren, MI • On-site

$97K - $134K/yr

Contractor

Posted 15 days ago

Job description

Optimization Engineer / Senior Optimization Engineer
Seeking a highly motivated engineer or scientist with strong expertise in multi-objective optimization applied to complex engineering problems. This role will focus on developing, implementing, and deploying optimization methods to support engineering design, analysis, and decision-making across multidisciplinary applications.
Key Responsibilities
Develop and apply multi-objective optimization methods for engineering problems involving tradeoffs among performance, cost, mass, durability, efficiency, or other attributes
Build, validate, and improve optimization models for simulation-driven and data-driven engineering applications
Formulate engineering problems as mathematical optimization models, including objectives, constraints, and decision variables
Use and integrate commercial optimization tools and solvers as well as custom-developed optimization codes
Work with cross-functional engineering teams to translate real-world design challenges into robust optimization workflows
Analyze Pareto-optimal solutions and provide engineering insights to support decision-making
Support model calibration, sensitivity studies, design space exploration, and surrogate/model-reduction approaches where appropriate
Document methods, assumptions, and results, and communicate findings clearly to technical and non-technical stakeholders
Required Qualifications
Master's or Ph.D. in Mechanical Engineering, Aerospace Engineering, Industrial Engineering, Applied Mathematics, Operations Research, Computer Science, or a related field
Strong experience applying multi-objective optimization to engineering problems
Experience in optimization model development
Experience using commercial optimization software, solvers, or frameworks
Strong understanding of mathematical optimization techniques such as gradient-based optimization, nonlinear programming, evolutionary algorithms, surrogate-based optimization, or mixed-integer optimization
Experience working with simulation-based engineering tools and computational models
Strong programming and problem-solving skills
Preferred Qualifications
Ph.D. with demonstrated research in engineering optimization or related fields
Experience with commercial optimization codes such as CPLEX, Gurobi, modeFRONTIER, HEEDS, LS-OPT, iSight, GT-SUITE optimizer, or similar tools
Experience with surrogate modeling, reduced-order modeling, or multi-fidelity optimization
Experience in automotive, CAE, crashworthiness, thermal, structural, manufacturing, or multidisciplinary engineering optimization, simulation software such as LS-DYNA, ABAQUS.
Ability to work in a collaborative environment and influence technical direction across teams
Desired Technical Skills
Multi-objective optimization and Pareto tradeoff analysis
Engineering model formulation and simulation-based optimization
Commercial and custom optimization code development
Numerical methods, statistics, and design of experiments
Python, MATLAB, C++, or similar technical computing languages
Familiarity with finite element, multiphysics, or system-level engineering models

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