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FR
Fraunhofer-Gesellschaft
last mo.

Master Thesis - Out-of-distribution detection + annotation for traversability estimation for robots(m/w/x)

Stuttgart
Full-timeInternshipOn-site
AI/ML
Data Science

Description

In this role, you will develop advanced deep learning models to enhance robot navigation in diverse outdoor environments. Your work will focus on improving traversability estimation and adapting algorithms to new terrains, ensuring safe and efficient robot operations.

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Requirements

  • •Field of study in automation technology, electrical engineering, computer science, cybernetics, mechanical engineering, mathematics, mechatronics, control engineering, software design, software engineering, technical computer science, or comparable
  • •Enrollment at a German university/Hochschule
  • •Background in computer science, software engineering, mechatronics, or similar
  • •Experience with deep learning frameworks such as Keras, TensorFlow, or PyTorch
  • •Experience in developing and testing deep learning models for computer vision applications is beneficial
  • •Analytical mindset
  • •Enthusiasm for mobile robotics
  • •Fluency in English or German

Education

Currently in higher education

Tasks

  • •Design a semantic traversability classification DNN pipeline
  • •Extend the few-shot segmentation DNN algorithm for new environments
  • •Evaluate methods to identify out-of-distribution domains
  • •Assess approaches for automated model learning using robot experience
  • •Test implementation in real-world scenarios with CURT robots

Tools & Technologies

KerasTensorFlowPyTorch

Languages

English – Business Fluent

German – Business Fluent

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