Master's Thesis Opportunity in 3D Scene Graphs

vor 4 Wochen

Renningen, Baden-Württemberg, Deutschland Bosch Vollzeit
Position Overview

As part of your master's thesis, you will engage in innovative research focused on open-vocabulary 3D scene comprehension utilizing 3D scene graphs. Your primary responsibility will involve the incremental development and integration of these scene graphs.

You will play a vital role in enhancing 3D scene graph predictions across various domains, including:

  • Architectural design
  • Training data and loss function optimization
  • Model efficiency improvements
  • Real-world application of models

Additionally, you will assess your model's performance using publicly available datasets, compare it with leading-edge methodologies, and showcase its effectiveness on real-world data through a suitable robotics platform.

This position also offers the chance to publish your findings in reputable conferences, contributing to the academic community.

Qualifications

  • Education: Enrollment in a master's program in Computer Science or a related field, with a concentration in machine learning or deep learning, and a strong academic record.
  • Experience and Skills: Proficiency in Deep Learning, Computer Vision, and/or robotics, with solid programming abilities, particularly in Python or C++. Familiarity with Deep Learning frameworks such as PyTorch or Jax is essential.
  • Personal Attributes: A motivated and inquisitive individual, eager to explore cutting-edge topics in machine learning research, robotics, and autonomous driving technologies.
  • Language Proficiency: Fluent in English; knowledge of German is a plus.

Additional Information

Duration: 6 months

This thesis requires current enrollment at a university. Please submit your CV, academic transcripts, examination regulations, and, if applicable, a valid work and residence permit.

At Bosch, we value diversity and inclusion as integral components of our corporate culture. We encourage applications from all individuals, regardless of gender, age, disability, religion, ethnic background, or sexual orientation.

For further inquiries regarding this position:
Contact Sebastian Koch from the Functional Department.

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