Machine Learning Expert in Uncertainty Quantification

7 days ago


Kongens Lyngby, Lyngby-Tårbæk Kommune, Denmark DTU - Technical University of Denmark Full time

**Key Responsibilities:

The successful candidate will design and develop novel deep learning techniques for semantic segmentation and probabilistic defect detection onboard aerial drones operating in confined environments. This includes:

  • Conducting state-of-the-art research and developing uncertainty-aware models that enhance inspection reliability and robustness in challenging scenarios.
  • Creating and curating probabilistic annotated datasets to capture ambiguous defect information.
  • Leveraging model outputs to derive reproducible qualitative and quantitative metrics of faults and defects.
  • Collaborating with a multidisciplinary team to integrate algorithms into a practical aerial inspection platform.

Requirements:

  • A two-year master's degree or similar degree with an academic level equivalent to a two-year master's degree.
  • Prior work with probabilistic deep learning, Bayesian inference, and uncertainty quantification in computer vision.
  • Experience with simulation tools and ROS.

Working Conditions:

  • The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations.
  • The allowance will be agreed upon with the relevant union.
  • You will have the opportunity to collaborate with a multidisciplinary team and contribute to the development of cutting-edge technologies.


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