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Computational Modeling Simulation Multiphysics Jobs in Tennessee

Marmon Holdings, Inc.

Senior Thermal Engineer

Moscow, TN · On-site

$94K - $129K/yr

Translate laboratory and field test data into validated computational models for production use * Own the accuracy and consistency of engineering calculations across selection and simulation tools

Marmon

Senior Thermal Engineer

Moscow, TN · On-site

$94K - $129K/yr

Translate laboratory and field test data into validated computational models for production use * Own the accuracy and consistency of engineering calculations across selection and simulation tools

Type One Energy

Mechanical Design Engineer

Knoxville, TN · On-site

$72K - $98K/yr

Develop and validate Finite Element (FEA) and Computational Fluid Dynamics (CFD) models using ANSYS ... Analyze and interpret simulation results to deliver actionable insights for design optimization and ...

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Computational Modeling Simulation Multiphysics information

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.
What are popular job titles related to Computational Modeling Simulation Multiphysics jobs in Tennessee? For Computational Modeling Simulation Multiphysics jobs in Tennessee, the most frequently searched job titles are:
What job categories do people searching Computational Modeling Simulation Multiphysics jobs in Tennessee look for? The top searched job categories for Computational Modeling Simulation Multiphysics jobs in Tennessee are:
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Computational Modeling Simulation Multiphysics jobs near you
Postdoctoral Research Associate - Data Scientist

Postdoctoral Research Associate - Data Scientist

Oak Ridge National Laboratory

Oak Ridge, TN • On-site

Full-time

Posted 15 hours ago

Oak Ridge National Laboratory rating

9.3

Company rating: 9.3 out of 10

Based on 15 frontline employees who took The Breakroom Quiz

3rd of 103 rated laboratories

Job description

Requisition Id 16485
Overview:
The Data and AI Systems Research Section within the Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL) is seeking a postdoctoral researcher to join the Workflow Systems Group and help advance the use of AI in scientific discovery. This position centers on scientific machine learning, automated AI/ML optimization, and high-performance computing (HPC), with an emphasis on developing intelligent systems that can accelerate large-scale scientific research on leadership-class supercomputers.
The successful candidate will contribute to research efforts supported by the U.S. Department of Energy Office of Science, including the Advanced Scientific Computing Research (ASCR) program and the Genesis initiative. These programs focus on integrating AI directly into scientific workflows to enable autonomous, data-driven discovery in areas such as fusion energy, materials science, climate science, and nuclear energy.
As part of ORNL's interdisciplinary research environment, you will work alongside scientists, engineers, and computational researchers while leveraging world-class computing resources, including Frontier, the world's first exascale supercomputer. The role includes developing and advancing open-source software for large-scale hyperparameter optimization (HPO), neural architecture search (NAS), and Bayesian optimization on distributed HPC systems.
Research activities will address key challenges in AI for science, including surrogate modeling, uncertainty quantification, and multi-fidelity optimization for complex simulation workflows. This position offers an opportunity to contribute to cutting-edge AI and HPC research while supporting DOE's broader mission to advance scientific innovation through computational science.
The appointment length is 2 years with the possibility of extension, subject to performance and availability of funding.
Major Duties and Responsibilities:
  • Conduct research and development in scalable AI/ML methods for scientific computing and high-performance computing environments.
  • Develop and evaluate optimization techniques for machine learning workflows, including approaches for model tuning, automated model design, and adaptive search strategies.
  • Contribute to research in uncertainty quantification, surrogate modeling, and other methods that improve the robustness and reliability of AI-driven scientific applications.
  • Design and implement distributed and parallel approaches that efficiently leverage large-scale computing resources, including heterogeneous CPU/GPU systems, along with the possibility of working with Quantum computing.
  • Collaborate with interdisciplinary research teams to integrate AI/ML capabilities into scientific simulation, data analysis, and computational workflows.
  • Contribute to the development and maintenance of open-source software, including testing, documentation, and user support activities.
  • Work closely with researchers and domain scientists to communicate results, define research directions, and support collaborative projects.
  • Publish research findings in peer-reviewed journals and present work at scientific workshops and conferences.
  • Design and implement scalable AI/ML optimization algorithms for hyperparameter optimization and neural architecture search, targeting scientific machine learning models running on leadership-class HPC systems.
  • Deliver ORNL's mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace - in how we treat one another, work together, and measure success.

Basic Qualifications:
  • A PhD in Computer Science, Applied Mathematics, Computational Science, Data Science, or a related discipline completed within the last three years.
  • An excellent record of productive and creative research as demonstrated by publications in top peer-reviewed journals and conferences.
  • Demonstrated experience with machine learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn) and hyperparameter optimization or AutoML techniques.
  • Proficiency in Python and familiarity with software engineering best practices (version control, testing, documentation).
  • Experience with HPC environments and parallel/distributed computing.
  • Strong problem-solving and communication skills, with the ability to work collaboratively in a team setting.

Preferred Qualifications:
  • Experience with multi-fidelity optimization, neural architecture search, or large-scale AutoML systems.
  • Familiarity with surrogate modeling, physics-informed neural networks, or uncertainty quantification for scientific applications.
  • Prior exposure to DOE workflows, national laboratory environments, or large-scale simulation codes.
  • Experience contributing to open-source scientific software projects.

#LI-DC1
This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.
We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.
If you have trouble applying for a position, please email ORNLRecruiting@ornl.gov.
ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply. UT-Battelle is an E-Verify employer.

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