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Machine Learning Cfd Jobs in Washington, DC (NOW HIRING)

Naval Architect

Washington, DC · On-site

$77K - $128K/yr

Performing computational fluid dynamics (CFD) and finite element analyses (FEA) to assess system ... Needs to occasionally move about inside the office to access file cabinets, office machinery, etc.

Naval Architect

Washington, DC · On-site

$77K - $128K/yr

Performing computational fluid dynamics (CFD) and finite element analyses (FEA) to assess system ... Needs to occasionally move about inside the office to access file cabinets, office machinery, etc.

... machine learning (ML), and augmented reality (AR). QinetiQ US's dedicated experts in defense ... Computational Fluid Dynamics (CFD) * Experience in additive manufacturing design, methods, and ...

Mechanical Engineer

Lorton, VA · On-site

$91K - $116K/yr

... machine learning (ML), and augmented reality (AR). QinetiQ US's dedicated experts in defense ... Computational Fluid Dynamics (CFD) * Experience in additive manufacturing design, methods, and ...

... machine learning (ML), and augmented reality (AR). QinetiQ US's dedicated experts in defense ... Computational Fluid Dynamics (CFD) * Experience in additive manufacturing design, methods, and ...

Mechanical Engineer

Lorton, VA · On-site

$91K - $116K/yr

... machine learning (ML), and augmented reality (AR). QinetiQ US's dedicated experts in defense ... Computational Fluid Dynamics (CFD) * Experience in additive manufacturing design, methods, and ...

... machine learning (ML), and augmented reality (AR). QinetiQ US's dedicated experts in defense ... Computational Fluid Dynamics (CFD) * Experience in additive manufacturing design, methods, and ...

... machine learning (ML), and augmented reality (AR). QinetiQ US's dedicated experts in defense ... Computational Fluid Dynamics (CFD) * Experience in additive manufacturing design, methods, and ...

Mechanical Engineer

Lorton, VA · On-site

$91K - $116K/yr

... machine learning (ML), and augmented reality (AR). QinetiQ US's dedicated experts in defense ... Computational Fluid Dynamics (CFD) * Experience in additive manufacturing design, methods, and ...

Machine Learning Cfd information

See Washington, DC salary details

$12.5K

$105.3K

$149.5K

How much do machine learning cfd jobs pay per year?

As of Jul 30, 2026, the average yearly pay for machine learning cfd in Washington, DC is $105,348.00, according to ZipRecruiter salary data. Most workers in this role earn between $96,800.00 and $124,600.00 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.
Infographic showing various Machine Learning Cfd job openings in Washington, DC as of June 2026, with employment types broken down into 2% As Needed, 69% Full Time, 16% Part Time, 3% Temporary, 8% Contract, and 2% Nights. Highlights an 91% Physical, 4% Hybrid, and 5% Remote job distribution, with an average salary of $105,348 per year, or $50.6 per hour.

Postdoctoral Fellow (PREP0004401)

Johns Hopkins University

Gaithersburg, MD • On-site

$53K - $72K/yr

Full-time

Re-posted 5 days ago


Johns Hopkins Medicine rating

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Company rating: 7.5 out of 10

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Job description

Description
PREP Research Associate
CHIPS Funded Project.

This position is part of the National Institute of Standards and Technology (NIST) Professional Research Experience Program (PREP). NIST recognizes that its research staff may want to collaborate with researchers at academic institutions on specific projects of mutual interest and, therefore, requires those institutions to be recipients of a PREP award. The PREP program involves staff from a wide range of backgrounds conducting scientific research across various fields. Individuals in this position will perform technical work supporting the collaboration's scientific research.
Research Title:
Digital Twins for Atomic Layer Deposition Processes
The work will entail:
The Chemical Sciences Division of the National Institute of Standards and Technology (NIST) is seeking a postdoctoral researcher to join a multi-disciplinary team of scientists working on advanced modeling of atomic layer deposition (ALD) processes. The project will develop a suite of in situ metrology tools to quantify mass transport processes occurring during thermal ALD. Metrology data will support the development of computational fluid dynamics (CFD) and machine learning (ML) models of transport and surface reaction processes, with the ultimate goal of producing validated digital twins for thermal ALD processes. The project team includes experts in vapor phase deposition processes, in situ optical metrology, micro-electromechanical system (MEMS) design, numerical modeling, and machine learning. NIST maintains unique facilities for the development and application of fast and sensitive in situ diagnostics for vapor phase deposition processes. Multiple research-grade ALD systems dedicated to oxide, metal, and sulfide film deposition are available, as well as optical measurements spanning the UV and mid-IR wavelengths tailored for surface and gas-phase processes.
U.S. Citizen Preferred
Key responsibilities will include but are not limited to:
• Operating and maintaining multiple ALD system and custom in situ optical setups
• Performing temporally- and spatially-resolved optical measurements during ALD
• Using and analyzing optical process data to validate transport models
• Publishing findings in peer-reviewed scientific journals and presenting at conferences
Qualifications
• Hands-on expertise in the design and assembly of research-grade ALD reactors
• Expertise in building and maintaining custom lab instrumentation
• Expertise in vapor phase deposition processes such as ALD
• Safe operation and maintenance of ALD systems
• Operation of in situ process diagnostics, including custom optical setups
• Knowledgeable in vapor transport mechanisms, precursor chemistry, and process development/optimization applicable to ALD
• Ph.D. or equivalent experience in chemistry, physics, materials science, or related field
• Strong written and oral communication skills
• Ability to work as part of a team and independently
Application Instructions
Please upload the following with your application:
• CV/Resume
*Please limit C.V to 3 pages only and ONLY include a valid email address for your contact info. Your resume will not be considered if the following information is included on your CV/resume.
Self portraits
Phone number
Home address/Country
Citizenship status
Languages spoken
Sex/Gender
Privacy Act Statement
Authority: 15 U.S.C. § 278g-1(e)(1) and (e)(3) and 15 U.S.C. § 272(b) and (c)
Purpose: The National Institute for Standards and Technology (NIST) hosts the Professional Research Experience Program (PREP) which is designed to provide valuable laboratory experience and financial assistance to undergraduates, post-bachelor's degree holders, graduate students, master's degree holders, postdocs, and faculty.
PREP is a 5-year cooperative agreement between NIST laboratories and participating PREP Universities to establish a collaborative research relationship between NIST and U.S. institutions of higher education in the following disciplines including (but may not be limited to) biochemistry, biological sciences, chemistry, computer science, engineering, electronics, materials science, mathematics, nanoscale science, neutron science, physical science, physics, and statistics. This collection of information is needed to facilitate the administrative functions of the PREP Program.
Routine Uses: NIST will use the information collected to perform the requisite reviews of the applications to determine eligibility, and to meet programmatic requirements. Disclosure of this information is also subject to all the published routine uses as identified in the Privacy Act System of Records Notices: NIST-1: NIST Associates.
Disclosure: Furnishing this information is voluntary. When you submit the form, you are indicating your voluntary consent for NIST to use of the information you submit for the purpose stated. By applying to a CHIPS-funded PREP opportunity, you also acknowledge that participation in the project requires signing a Non-Disclosure Agreement (NDA) prior to beginning any work.

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