CFD Research Corporation
Machine Learning Engineer
CFD Research Corporation, Huntsville, Alabama, United States, 35824
Overview
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Machine Learning Engineer
role at
CFD Research Corporation .
CFD Research's Aerospace Data Sciences group is currently leading the development of a software called Sage. Sage's primary purpose is to train fast-running, machine learning-based surrogate models which approximate physics-based processes. Within the Aerospace Data Science group, the Applied Surrogate Modeling team is looking for an engineer to support development and refinement of machine learning models to predict performance of a diverse set of systems relevant to the Department of Defense (DoD), including aerospace, ground, and naval systems. The trained machine learning models will feature rapid inference times and be employed as surrogates to more expensive physics-based solutions. As new capabilities are needed, the engineer will support software development of new and existing model architectures.
The Applied Surrogate Modeling team is a small group of multi-disciplinary engineers that focuses on the development and deployment of fast-running, data-driven surrogate models for mission-specific applications.
Responsibilities
Generating DoD system performance data using physics-based tools such as computational fluid dynamics (CFD) and finite element methods (FEM)
Training surrogate models to approximate these physics-based processes
Deploying these surrogate models into systems that require high inference rates (1+ kHz)
Training data-driven surrogate models that approximate a wide variety of physics-based systems using the Sage software
Exporting and deploying surrogate models into high-throughput applications such as 6DOF simulators, hardware/software-in-the-loop systems, and optimization workflows
Demonstrating, validating, and verifying surrogate model accuracy
Working with other engineers to guide and inform physics-based data collection using design of experiments techniques
Supporting development of new surrogate modeling techniques where necessary
Location Location:
This role can be based in either Huntsville, AL, or Dayton, OH and is 100% onsite.
Qualifications
Candidate must be a U.S. Citizen and meet eligibility to obtain/maintain a SECRET Clearance
Master's in Aerospace or Mechanical Engineering, Computer Science, or similar
Proficiency with Python and Linux-based operating systems
Experience working with coupled, multi-physics simulations and data including: external aerodynamics, heat transfer, structural modeling, and aerothermoelasticity
Familiarity with machine-learning and surrogate-modeling techniques such as: artificial neural networks (ANNs), Kriging, and Gaussian process regression
Desired Qualifications
Ph.D. in Aerospace or Mechanical Engineering, or similar discipline
Proficiency with compiled languages, especially C++ and Fortran
Experience with the PyTorch machine learning library
Knowledge of formal gradient-based and gradient-free optimization techniques
Experience with multi-disciplinary analysis and optimization (MDAO)
Active SECRET clearance
Benefits CFD Research offers competitive salaries and excellent employee benefits, including an employer matching 401(k) and Employee Stock Ownership Plan (ESOP). CFD Research offers a highly competitive insurance package, including medical, vision, and dental insurance. We offer company paid leave, compensation time, parental leave, long-term and short-term disability, accidental death and dismemberment, and life insurance. Performance appraisals occur twice a year and annual pay increases are based upon corporate goals, personal development, performance, and outstanding achievements. In addition, group and individual bonuses are awarded for exceptional performance.
CFD Research is an EO employer - Veterans/Disabled and other protected categories.
Seniority level Not Applicable
Employment type Full-time
Job function Software Development
Industries Software Development
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Machine Learning Engineer
role at
CFD Research Corporation .
CFD Research's Aerospace Data Sciences group is currently leading the development of a software called Sage. Sage's primary purpose is to train fast-running, machine learning-based surrogate models which approximate physics-based processes. Within the Aerospace Data Science group, the Applied Surrogate Modeling team is looking for an engineer to support development and refinement of machine learning models to predict performance of a diverse set of systems relevant to the Department of Defense (DoD), including aerospace, ground, and naval systems. The trained machine learning models will feature rapid inference times and be employed as surrogates to more expensive physics-based solutions. As new capabilities are needed, the engineer will support software development of new and existing model architectures.
The Applied Surrogate Modeling team is a small group of multi-disciplinary engineers that focuses on the development and deployment of fast-running, data-driven surrogate models for mission-specific applications.
Responsibilities
Generating DoD system performance data using physics-based tools such as computational fluid dynamics (CFD) and finite element methods (FEM)
Training surrogate models to approximate these physics-based processes
Deploying these surrogate models into systems that require high inference rates (1+ kHz)
Training data-driven surrogate models that approximate a wide variety of physics-based systems using the Sage software
Exporting and deploying surrogate models into high-throughput applications such as 6DOF simulators, hardware/software-in-the-loop systems, and optimization workflows
Demonstrating, validating, and verifying surrogate model accuracy
Working with other engineers to guide and inform physics-based data collection using design of experiments techniques
Supporting development of new surrogate modeling techniques where necessary
Location Location:
This role can be based in either Huntsville, AL, or Dayton, OH and is 100% onsite.
Qualifications
Candidate must be a U.S. Citizen and meet eligibility to obtain/maintain a SECRET Clearance
Master's in Aerospace or Mechanical Engineering, Computer Science, or similar
Proficiency with Python and Linux-based operating systems
Experience working with coupled, multi-physics simulations and data including: external aerodynamics, heat transfer, structural modeling, and aerothermoelasticity
Familiarity with machine-learning and surrogate-modeling techniques such as: artificial neural networks (ANNs), Kriging, and Gaussian process regression
Desired Qualifications
Ph.D. in Aerospace or Mechanical Engineering, or similar discipline
Proficiency with compiled languages, especially C++ and Fortran
Experience with the PyTorch machine learning library
Knowledge of formal gradient-based and gradient-free optimization techniques
Experience with multi-disciplinary analysis and optimization (MDAO)
Active SECRET clearance
Benefits CFD Research offers competitive salaries and excellent employee benefits, including an employer matching 401(k) and Employee Stock Ownership Plan (ESOP). CFD Research offers a highly competitive insurance package, including medical, vision, and dental insurance. We offer company paid leave, compensation time, parental leave, long-term and short-term disability, accidental death and dismemberment, and life insurance. Performance appraisals occur twice a year and annual pay increases are based upon corporate goals, personal development, performance, and outstanding achievements. In addition, group and individual bonuses are awarded for exceptional performance.
CFD Research is an EO employer - Veterans/Disabled and other protected categories.
Seniority level Not Applicable
Employment type Full-time
Job function Software Development
Industries Software Development
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