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Machine Learning Cfd Jobs in Ohio (NOW HIRING)

Machine Learning Cfd information

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.

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 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.

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 popular job titles related to Machine Learning Cfd jobs in Ohio? For Machine Learning Cfd jobs in Ohio, the most frequently searched job titles are:
What job categories do people searching Machine Learning Cfd jobs in Ohio look for? The top searched job categories for Machine Learning Cfd jobs in Ohio are:
What cities in Ohio are hiring for Machine Learning Cfd jobs? Cities in Ohio with the most Machine Learning Cfd job openings:

Research Engineer I - High Speed Systems

ARCTOS Technology Solutions

Dayton, OH • On-site

$90K - $95K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 2 days ago


Job description

ARCTOS Technology Solutions, LLC

arctos-us.com/careers/



OVERVIEW

Reach New Heights!


ARCTOS Technology Solutions, LLC (ARCTOS) is a fast-growing, technology-oriented small business providing aerospace, defense, and digital solutions, with offices and work sites across the United States. We’re looking for team-oriented innovators eager to tackle interesting challenges, work on important problems, and receive great benefits and employee support.


REQUIREMENT

ARCTOS is seeking a highly skilled and motivated Research Engineer to join our team supporting AFRL’s High Speed Vehicles Division. This role is focused on supporting the development of reusable hypersonic vehicle technologies specifically through advancing data-driven surrogate modeling capabilities to efficiently predict distributed and integrated aerodynamic loads as a function of vehicle deformation. The position involves developing reduced-order models, integrating multi-fidelity computational and experimental datasets, and applying hybrid physics/machine learning approaches to capture nonlinear and unsteady aerodynamic behavior. The successful candidate will combine expertise in aerothermoelastic modeling, machine learning, and reduced-order modeling to enable rapid, high-fidelity analysis for aeroelastic and aerothermodynamic applications.


PRIMARY RESPONSIBILITIES

The Research Engineer will work with Government customers, other ARCTOS personnel, and other Government organizations and contractors in the performance of the following duties:

Surrogate Model Development & Reduced-Order Modeling

Develop and implement surrogate models, emphasizing interpolation-based approaches (IROMs) such as neural networks. Investigate methods including residual networks, recurrent models, and neural operators to capture nonlinear and unsteady aerodynamics. Support CFD data generation and evaluate model performance across flight conditions and structural deformations.

Data Fusion & Multi-Fidelity Integration

Integrate high- and low-fidelity computational data with experimental datasets into a unified framework. Apply hybrid physics/machine learning approaches, including residual learning and layered surrogates, to balance accuracy and efficiency and improve generalization across geometries and operating regimes.

Model Assessment, Application & Transition

Evaluate model accuracy and efficiency across vehicle configurations and trajectories in coordination with AFRL. Synthesize trends in aerodynamic performance versus deformation and support transition of models into operational environments, including validation, documentation, and participation in technical reviews.

KNOWLEDGE AND SKILLS

  • The well-qualified candidate will have expertise in many of these areas:
    • Experience with reduced-order modeling techniques
    • Experience with finite element analysis, high-performance computing
    • Knowledge of CAD, AFSIM, FEA programs, Python
  • Understanding of aerothermoelastic analysis for hypersonic vehicles.
  • Excellent organizational and time management abilities
  • Desire to work both independently and in a team environment as the project requires
  • Excellent verbal and written communication skills
  • Proven track record in conducting both applied and fundamental research, evidenced by publications, patents, or successful technology demonstrations.

EDUCATIONAL, CLEARANCE AND CERTIFICATION

  • Ph.D. in Mechanical Engineering, Aerospace Engineering, Materials Science, or a closely related field.
  • U.S. Citizenship is required

PHYSICAL/WORKING ENVIRONMENT

  • Primary work environment is a standard office setting.


TRAVEL

  • Occasional travel is expected and will be performed under the guidelines of Federal Travel Regulations (FTR) and/or Joint Travel Regulations (JTR)


BENEFITS

401(k) Retirement Plan with Company Matching; Health Insurance & HSA; Dental & Vision Insurance; Company Paid Life Insurance, AD&D and Short-Term Disability; Paid Time Off, Volunteer Time Off; Employee Assistance Program




In compliance with pay transparency requirements, the salary range is not a guarantee of compensation or salary, as final offer amount may vary based on factors including but not limited to experience and geographic location.

ARCTOS and its subsidiaries are an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status. We look forward to reviewing your application!