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Paid Internship
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24Open Positions

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Internship - Enhancing a Diffusion-Based Traffic Scene Generation Model for Closed-Loop Simulation - REF5865M

AUMOVIO
Found 2 months ago
Location
Berlin, Germany
Duration (Months)
6 Months
Time
Full-time
Work Mode
Not disclosed
Salary
A performance-based remuneration for your work with us
Visa Help
Not disclosed
Last Verified
1 month ago

Education

  • Bachelor

Skills & Qualifications

Technical Skills

  • Python
  • deep learning frameworks
  • PyTorch
  • diffusion models
  • generative modeling

Soft Skills

  • Independent
  • reliable
  • collaborative working style
  • problem-solving skills

Job Description

High-fidelity traffic scenario generation is increasingly important for developing and validating autonomous driving systems. Diffusion models have recently demonstrated strong potential for synthesizing diverse and realistic multi-agent driving scenes. In this internship, you will contribute to advancing an existing diffusion-based traffic scene generation model used in closed-loop simulation within the Aumovio AI Lab in Berlin. The focus of your work will be on improving the model's generation quality through enhanced training strategies, better data representations, and the development of a critic module for automated quality assessment. You will have direct access to our simulation stack, enabling interactive testing and evaluation of your improved models across a wide range of simulated driving scenarios.

Requirements

  • Enrolled students (m/f/diverse) in Computer Science, Electrical Engineering, Robotics, AI, or a related field.
  • Solid programming skills in Python and experience with deep learning frameworks (preferably PyTorch).
  • Prior experience with diffusion models or generative modeling is a strong advantage.
  • Strong command of English (spoken and written); German is a plus.
  • Independent, reliable, and collaborative working style with strong problem-solving skills.

Related Field

  • AI & Machine Learning

Related Subfield

  • AI Research

Languages

  • English

Nice to Haves

  • German
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