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Machine Learning Cfd Jobs in Ohio (NOW HIRING)

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Machine Learning Cfd information

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
Accelerated Leadership Program - R&D Career Path - AI Simulation (Begins June 2027)

Accelerated Leadership Program - R&D Career Path - AI Simulation (Begins June 2027)

Schaeffler

Wooster, OH • On-site

Full-time

Re-posted 10 days ago

Schaeffler rating

7.1

Company rating: 7.1 out of 10

Based on 53 frontline employees who took The Breakroom Quiz

294th of 430 rated machine equipment manufacturers

Job description

Job Summary:
Schaeffler is a dynamic global technology company that partners with major automobile manufacturers and key players in the aerospace and industrial sectors. They are seeking candidates for their Accelerated Leadership Program, which includes four six-month rotations focused on AI Simulation, providing cross-departmental exposure and guaranteed placement in a permanent engineering position upon graduation.
Responsibilities:
• Partner with a mentor who is an experienced professional leader to develop best practices and evolve into a leader at Schaeffler
• Gain knowledge alongside experts in relevant and cross-functional departments
• Simulation workflow optimization through scripting and AI
• Investigate the creation of custom AI agents and simulation inferencing through model training (PyTorch, Tensorflow, etc.)
• Post-processing of results for report automation
• Trained based on the 70:20:10 model which emphasizes a mixture of hands-on experience, mentoring, and formal training
• Leadership training and tools to develop leadership skills
• Complete rotation development milestones
• Company housing for the duration of the program (two years) and a competitive salary and benefits
• Become equipped for your guaranteed position upon graduation
Qualifications:
Required:
• Bachelor's Degree in relevant Engineering or Engineering Technology
• 3.0 minimum GPA
• At least two rotations of relevant co-ops or internships
• Strong background in machine learning, utilization of AI in engineering
• Exposure to FEA/CFD/process simulations with commercial CAD and simulation tools
• Thorough understanding of engineering principals
• Excellent Organization and Communication skills
• Strong Microsoft Office skills
• Willingness to relocate every 6 months
Preferred:
• Experience in a fast-paced environment
• Leadership experience or exposure
Company:
Schaeffler is a supplier of automotive and industrial sectors for inventions and developments in the fields of motion and mobility. Founded in 1964, the company is headquartered in Herzogenaurach, DEU, with a team of 10001+ employees. The company is currently Late Stage.

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