Aerodynamics/Machine Learning Intern
Aerodynamics/Machine Learning Intern

Location: Nashville, TN

About the Role

Whisper Aero is seeking a Masters level student pursuing a degree in aerospace or mechanical engineering. The purpose of this role is to develop machine learning interfaces for design/optimization of aerodynamic applications and/or prediction from compressor cascade / CFD analysis tools.

What You’ll Do

Implement machine learning techniques to accelerate the aerodynamic design process effectively searching through high dimensional spaces and optimizing in reduced order spaces

Research and improve current visualization processes of large data sets through automated data reduction tools

Help with uncertainty quantification in design

Extend AI procedures to other disciplines (structures, powertrain etc) and demonstrate efficacy in a multidisciplinary workflow

Basic Qualifications

US Person Status

Masters level student pursuing degrees in aerospace or mechanical engineering, data science, applied mathematics

Familiarity with deep learning architectures like Support Vector Machines, Random Forests, Gene Expression Programming, Convolution Neural Nets, Multi-Layer Perceptrons involving supervised and unsupervised learning specifically geared for aerospace applications.

Expertise in Python, Git, MongoDB/SQL and one of the following: TensorFlow, scikit-learn, PyTorch

Bonus Qualifications

Compressor and or Rotorcraft Aerodynamics

Some CFD Experience especially modeling of internal propulsor /external aerodynamic turbulent flowfields

Application Process

careers@whisper.aero⁠

with the following subject line. If you are interested in more than 1 role, please indicate that in the body of your email. Please confirm in your application email that you are a US Person (citizen or permanent resident).

[name_of_the_role] first_name last_name for Whisper Aero

ex. [CFD Engineer] Jane Doe for Whisper Aero

Attach reference materials

• Resumé (required for consideration)

• Portfolio of prior work (optional)

• Links to your LinkedIn, website, and/or Github repo (optional)

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