Intern - ML for Computational Fluid Dynamics

Posted Yesterday
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Zürich, CHE
In-Office
Internship
Aerospace • Artificial Intelligence • Defense • Manufacturing
The Role
Develop and implement machine learning, Bayesian optimization, and numerical algorithms to improve CFD-based aerodynamic design for unmanned vehicles. Evaluate algorithms on real-world simulations, assess results, present findings, and deploy solutions into the design pipeline. The role involves collaboration between aerodynamics and ML teams, with work spanning research, software development, and engineering implementation.
Summary Generated by Built In

Are you interested in applying machine learning (ML) and numerical optimization to improve Computational Fluid Dynamics (CFD) for aerodynamic design? Always wondered how ML can be applied to engineering? If you can dedicate to us at least 5 months, then this is your chance to join a world-leading team at the forefront of UAV design and:

  • Work at the intersection of the aerodynamics and ML teams to improve the design of our unmanned vehicles.
  • Research and develop ML and numerical algorithms to guide the CFD exploration of aerodynamic design space.
  • Evaluate your ideas on real-world test cases, assess the results, and present your findings and conclusions.
  • Implement and deploy the algorithms in our design pipeline.

Requirements

Familiarity with Gaussian processes and Bayesian optimization.

  • A desire to push the boundaries of ML for engineering and extend mathematical concepts with minimal supervision.
  • Hands-on experience in developing Python code for shared repositories: git, code reviews, continuous integration.
  • Knowledge of mathematical optimization concepts and algorithms, e.g., convex optimization, non-linear programming, etc.
  • Mindset to take ownership of your work and follow it from concept to final implementation. (Experience in research is a plus.)
  • Experience with CFD, large-scale simulations, and other ML is a plus.

Skills Required

  • Commit to at least five months.
  • Familiarity with Gaussian processes and Bayesian optimization.
  • Desire to advance machine learning for engineering and extend mathematical concepts with minimal supervision.
  • Hands-on experience developing Python code for shared repositories.
  • Experience with Git, code reviews, and continuous integration.
  • Knowledge of mathematical optimization concepts and algorithms, including convex optimization and nonlinear programming.
  • Ownership mindset and ability to follow work from concept through final implementation.
  • Research experience.
  • Experience with computational fluid dynamics and large-scale simulations.
  • Additional machine learning experience.
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The Company
500 Employees
Year Founded: 2021

What We Do

Destinus is a European aerospace and defense company that designs and manufactures autonomous flight systems, including drones, strike-effectors, and counter-drone systems. They utilize a vertically integrated model, handling design, airframe engineering, propulsion, and AI software in-house. Their mission focuses on advancing aerospace technology across the speed spectrum to enhance defense capabilities and global connectivity, with a particular emphasis on scalable strike and air defense systems for European and allied forces.

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