Type of Final Thesis:
Master
Supervisor: Daniel Bogdoll
Research Group:
Applied Technical Cognitive Systems
Archive Number: 4.756
Status of Thesis:
Completed
Date of start: 2021-12-02
The AIFB and FZI deal with all kinds of issues related to autonomous driving. For the scaling of autonomous vehicles, the detection of anomalies, also called corner cases, is enormously important. This can be used to improve models (active learning) or to react live. We are investigating methods that combine camera and lidar sensor data and detect anomalies in relevant areas.
For this, we need support in numerous areas, where you can contribute according to your strengths and interests. Possible topics are Semantic Segmentation, Deep Generative Models, Scene Synthesis, Pseudo-Lidar or State-of-the-art Benchmarking with KITTI, WAYMO, CARLA etc. Please also apply if you are interested interested in a hiwi position, BA etc.
TASKS
- Literature research, analysis and evaluation of the state of the art
- Implementation and evaluation of selected algorithms in Python
WE OFFER
- An interdisciplinary working environment with partners from science and industry
- Challenging tasks in an exciting and highly up-to-date subject area
- Regular meetups with the students in my team
- In case of outstanding work, submission of a paper for publication at a conference
- Free, independent working style with short, structured weekly meetings for regular feedback
WE EXPECT
- Good Python programming skills (under Linux with Git)
- Theoretical knowledge in the area of Machine Learning / Deep Learning
- Practical experience with Tensorflow or PyTorch
- Independent thinking and working, motivation and commitment
- Fluent in English or German
- Bonus points are given for experience with (un)supervised methods, ROS, CARLA, ClearML and LaTeX
REQUIRED DOCUMENTS
- Two sentences about your motivation (included in the e-mail)
- Current transcript of records (and if available, bachelor's degree certificate)
- Curriculum vitae in tabular form
CONTACT