Student Research Assistant Position in Object Detection with Uncertainty to Tackle Shifted and Out-Of-Distribution Data

  • Position: Student Research Assistant (Hiwi)
  • Department: Institute of Applied Informatics and Formal Description Methods
  • Supervisor: Marcus Fechner
  • Location: Karlsruhe Institute of Technology (KIT)


About Us

At the research group “Applied Technical-Cognitive Systems”, we are at the forefront of deep learning in the context of applied machine intelligence. Our research is in the area of autonomous systems, from self-driving cars (CoCar NextGen, CoCar, and the shuttles Anna and Ella) to autonomous service robots. We utilize deep learning and other machine learning-based approaches to advance these fields.


Position Overview
  • Position: Student Assistant
  • Start Date: As soon as possible
  • Working Hours: 20 - 80 hours per month
  • Duration: 6 months with the possibility of extension


Job Description

Dealing with the real world is challenging, as changing weather conditions, new objects, situations, etc. alter the data observed by the model. In practice, this data distribution shift or out-of-distribution data makes the model unreliable and hinders the safe deployment of deep neural networks in critical use cases, for example, autonomous driving. Models that fail to generalize in such scenarios may result in dangerous or catastrophic behavior.

In this research, we want to investigate how we can integrate different methods to estimate uncertainty in popular object detection models, to reliably detect when the model might fail. For support on this topic, we are searching for a motivated student.

Your primary responsibilities will include:

  • Implementing and training deep learning models.
  • Reading related research papers and participating in discussions on the topic.
  • Collaborating with us on experimental design, execution, and evaluation.
  • Other tasks as assigned related to our research.


  • Current enrollment as a student at KIT.
  • Strong interest in deep learning and machine learning.
  • Knowledge of the programming language Python.
  • Knowledge of PyTorch and/or Tensorflow.
  • Experience in working with Linux and Git.
  • Motivated to read research papers.
  • Motivated, responsible, and a quick learner.
  • Speak either German and/or English.


What we Offer
  • Gain hands-on experience in the field of deep learning and conducting systematic research.
  • Work closely with experienced researchers and PhD candidates.
  • Weekly to bi-weekly meetings with supervisor.
  • Coding support and helpful supervision.
  • Flexible working hours to accommodate your class schedule and the option for working remotely.
  • Contribute to research projects and papers.
  • Access to top-of-the-line deep learning workstations with the latest GPUs.


How to Apply

If you are enthusiastic about deep learning and eager to contribute to our research, please send your application to marcus fechner∂kit edu with the following documents:

  1. Cover letter (0.25-0.5 pages): Why do you want to work on this topic? Why are you suitable for the position? (mention your interests and relevant skills).
  2. CV/Resume (max. 2 pages).
  3. Recent transcript of records / grading table.
  4. Optional: Any relevant coding or project portfolio.


If you have any questions or need further information, please contact marcus fechner∂kit edu. Natürlich auch gerne auf Deutsch :)