Stelle ist zu besetzen ab: 15.01.2024

Beschreibung

We are seeking a motivated student assistant to join our project, "Data-Efficient Hybrid Multi-Task Approach for Object Detection in Real-World Applications", under the KiKIT program at the Karlsruhe Institute of Technology (KIT). This research focuses on enhancing vision systems by integrating data-driven methods with prior knowledge, emphasizing multi-task learning (MTL) to improve performance with minimal labeled data.

Responsibilities:
- Assist in collecting and preprocessing datasets for object detection, depth estimation, and semantic segmentation.
- Support the implementation and testing of MTL models, incorporating prior knowledge to optimize system robustness and efficiency.
- Conduct experiments to evaluate model performance across real-world scenarios.
- Document methodologies, experiments, and results for future research and reproducibility.

Requirements:
- Enrollment in a Master's program in Computer Science, Data Science, Electrical Engineering, or a related field.
- Proficiency in Python and experience with machine learning frameworks such as TensorFlow or PyTorch.
- Solid understanding of machine learning and computer vision concepts.
- Strong analytical skills and the ability to work both independently and collaboratively.

Benefits:
- Hands-on experience in advanced machine learning research.
- Flexible working hours to accommodate your academic schedule.
- A collaborative environment within a leading research institution.

Position Details: Duration: 40 hours per month for 6 months, with the possibility of extension.

Mode of Work: Hybrid, with primarily online collaboration depending on task requirements.

Application Process: Interested candidates should send their CV, a brief cover letter, and academic transcripts to moussa.sbeyti@kit.edu.