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Machine Learning Cfd Jobs in Chicago, IL (NOW HIRING)

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Machine Learning Cfd information

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$11.3K

$95.8K

$136K

How much do machine learning cfd jobs pay per year?

As of Jun 8, 2026, the average yearly pay for machine learning cfd in Chicago, IL is $95,819.00, according to ZipRecruiter salary data. Most workers in this role earn between $88,100.00 and $113,300.00 per year, depending on experience, location, and employer.

What are Machine Learning CFD jobs?

Machine Learning CFD (Computational Fluid Dynamics) jobs focus on integrating machine learning techniques with traditional fluid dynamics simulations and analyses. Professionals in this field use AI and data-driven models to accelerate simulations, improve prediction accuracy, and optimize fluid flow processes. These roles often require knowledge of both CFD principles and machine learning algorithms, and are commonly found in industries such as aerospace, automotive, and energy. Typical responsibilities include developing surrogate models for simulations, automating data analysis, and implementing deep learning approaches for complex flow problems.

How does a Machine Learning CFD professional typically collaborate with domain experts and software engineers in a project setting?

As a Machine Learning CFD (Computational Fluid Dynamics) professional, you’ll frequently collaborate with domain experts such as mechanical or aerospace engineers to ensure your models accurately reflect physical phenomena. You’ll also work closely with software engineers to integrate machine learning algorithms into simulation pipelines and optimize computational performance. Effective communication is key, as you’ll need to translate complex data-driven insights into actionable engineering solutions and vice versa. These collaborative efforts help streamline workflows, improve model accuracy, and ensure practical deployment of ML-enhanced CFD tools.

What is the difference between Machine Learning CFD vs Data Scientist?

AspectMachine Learning CFDData Scientist
Required CredentialsDegree in Engineering, Computer Science, or related fields; knowledge of CFD softwareDegree in Statistics, Computer Science, or related fields; strong programming skills
Work EnvironmentEngineering firms, aerospace, automotive industries, research labsBusiness, finance, tech companies, research institutions
Industry UsageSimulation, fluid dynamics, engineering analysisData analysis, predictive modeling, business insights

Machine Learning CFD focuses on applying machine learning techniques to computational fluid dynamics simulations, often within engineering contexts. Data Scientists analyze large datasets to extract insights and build predictive models across various industries. While both roles require programming skills and a strong analytical background, Machine Learning CFD emphasizes simulation and engineering applications, whereas Data Scientists focus on data-driven decision-making across diverse sectors.

What are the key skills and qualifications needed to thrive as a Machine Learning CFD (Computational Fluid Dynamics) Engineer, and why are they important?

To thrive as a Machine Learning CFD Engineer, you need a strong background in fluid dynamics, numerical methods, and machine learning, often supported by a degree in engineering, physics, or computer science. Familiarity with CFD software (such as ANSYS Fluent or OpenFOAM), programming languages like Python or C++, and machine learning frameworks (TensorFlow or PyTorch) is essential. Critical thinking, problem-solving, and effective communication are standout soft skills for interpreting data and collaborating on interdisciplinary teams. These competencies are crucial for developing innovative solutions that enhance simulation accuracy and computational efficiency in engineering projects.
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Machine Learning Cfd jobs near you
Postdoctoral Appointee-Developing an Exascale MuPhFASa (Multi Phase Flow Adaptive Simulator)

Postdoctoral Appointee-Developing an Exascale MuPhFASa (Multi Phase Flow Adaptive Simulator)

Argonne National Laboratory

Lemont, IL

$72K - $121K/yr

Full-time

Posted 22 days ago

Job description

The Argonne Leadership Computing Facility's (ALCF) mission is to accelerate major scientific discoveries and engineering breakthroughs for humanity by designing and providing world-leading computing facilities in partnership with the computational science community. We help researchers solve some of the world's largest and most complex problems with our unique combination of supercomputing resources and computational science expertise.

The Computational Science (CPS) Division focuses on solving the most challenging scientific problems through advanced modeling and simulation on the most capable computers. The division aims to build lab-wide cross-cutting simulation application capabilities integrating with mathematics, computer science, domain science, and advanced computing architectures and facilities. Additionally, the CPS provides an interdisciplinary home for spawning simulation programs and projects, often in collaboration with the ALCF.

The ALCF and CPS division are seeking a postdoctoral appointee to develop computational fluid dynamic (CFD) tools that make exascale computing accessible to a broader set of users. The successful candidate will develop a massively parallel solver, capable of running simulations on the Aurora supercomputer, using AMReX (https://amrex-codes.github.io/amrex/) and the lattice Boltzmann method (LBM). The candidate will develop flow/geometry-aware refinement strategies that go beyond traditional error estimators, creating physics-based adaptation algorithms that intelligently predict where refinement will be most beneficial for smarter, more efficient simulations. We seek someone with a strong CFD background and LBM expertise who can develop methodologies broad enough to tackle a diverse set of problems, appealing to a wide range of computational science users. Example problems are: bubbly flow, emulsions, sedimentation, wetting, gas-mixing, red-blood cell flow in the human vasculature. We are seeking a candidate who is intellectually curious and enthusiastic about computational research. They are intrinsically driven, goal-oriented, and can work collaboratively with others.

Working closely with the CPS divison, the postdoc will leverage AMReX and the LBM to develop an integrated framework to explore advanced workloads including simulations with in-situ visualization and, possibly, machine learning integration. This work will inform future ALCF platform procurement decisions.

Position Requirements

Required Skills:

  • Recent or soon-to-be-completed Ph.D. (typically completed in the last 5 years) in mechanical/aerospace/chemical engineering, applied mathematics or a related discipline.
  • Experience in numerical methods and CFD development using mesh-based scientific codes.
  • Expertise in the lattice Boltzmann method (LBM) as evidenced by their publications
  • High performance computing (HPC) experience in code development with parallel programming techniques using the message passing interface (MPI) library
  • Proficiency in writing code with C, C++ and/or Python
  • Ability to demonstrate strong written and oral communication skills
  • Ability to model Argonne's Core Values: Impact, Safety, Respect, Integrity, and Teamwork.

Desired Skills:

  • Experience with two-phase/multi-phase flows as evidenced by their publications
  • Experience programming GPUs with CUDA, SYCL, HIP or OpenMP
  • Experience using and developing code with AMReX
  • Experience in performance engineering to improve code scalability and reduce time-to-solution

Job Family

Postdoctoral

Job Profile

Postdoctoral Appointee

Worker Type

Long-Term (Fixed Term)

Time Type

Full timeThe expected hiring range for this position is $72,879.00-$121,465.00.

Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.

Click here to view Argonne employee benefits!

As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.

Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department.

All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.

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