Thesis - Machine Learning methods in the field of ADAS/AD

Your Responsibilities

An autonomous vehicle is a complex cyber-physical system that requires state-of-the-art ML/AI software components and sophisticated hardware equipment to work together. For the application of testing autonomous driving systems under different driving conditions, AVL is developing various machine learning components that are integrated into complex virtualized testing environment or used together with well-accepted ADAS/AD sensors, NVIDIA Pegasus AGX, NVIDIA Xavier AGX in the in-vehicle PC hardware.

• Development of new generation machine learning methods focused on the detection of object, lane and driving scenarios
• Integration of the developed methods into AVL platforms
• Testing and analysing full self-driving stacks under different hardware and software configurations with the integrated methods

Your Profile

• Ongoing studies in the fields of Computer Science, Telematics, Physics or Electrical Engineering
• Good programming skills in Python or C++
• Knowledge of Machine Learning and Computer Vision
• Knowledge of Big Data Analysis
• Good knowledge of German and English
• For this thesis is your presence at our headquarter in Graz required!

We offer

• You can write your thesis independently and receive professional guidance and support from our experienced employees.
• You will have the opportunity to exchange ideas with experts in the company and benefit from their expertise.
• Take the opportunity to immerse yourself in the world of AVL and embed your theoretical knowledge in a practical environment.

The successful completion of the thesis is remunerated with a one-time fee of 3.500,00 EUR before tax.

About AVL

AVL is one of the world's leading mobility technology companies for development, simulation and testing in the automotive industry, and beyond. The company provides concepts, solutions and methodologies in fields like vehicle development and integration, e-mobility, automated and connected mobility (ADAS/AD), and software for a greener, safer, better world of mobility.