Master Thesis Student (m/f/d) - Efficient AI for Autonomous Driving

DENSO; Eching am Ammersee; On-site; EUR 11,000 - 17,000; Full time

Benefits offered by this job

Home office

Health: wellbeing days & Urban Sports

Modern offices & canteen onsite

Open company culture

Regular company & team events

Job summary

DENSO invites a Master Thesis student to work on efficient AI for autonomous driving, focusing on evaluation and benchmarking of Vision Language Action systems. The project includes systematic assessment of VLA models and optimization techniques on realistic benchmarks.

The position is designed for students enrolled in a university for a Master Thesis; supervision from TUM in München can be provided. Home office options are available along with access to modern offices and campus amenities.

Qualifications

• Currently enrolled in a CS/engineering Master with a focus on data analysis, automotive, engineering or similar.
• Interest in autonomous driving and machine learning.
• Basic understanding of deep learning and model training.
• Ability to read and modify research codebases.
• Proficiency in Python.

Responsibilities

• Evaluate and benchmark VLA models for efficiency.
• Assist in researching optimization techniques for inference efficiency.
• Contribute to quantitative benchmarks on time and memory usage.
• Optional enhancements to VLA systems for inference efficiency.

Skills

Python

Deep Learning

Research Code

VLA experience

Autonomous driving

Education

Master in Computer Science/Engineering

Job description

Master Thesis Student (m/f/d) - Efficient AI for Autonomous Driving

Full-time

As an automotive supplier, DENSO is leading in developing and providing components and systems for heating, air conditioning, motor cooling, exhaust gas aftertreatment, automotive electrics and electronics and instrumentation.

Vision Language Action (VLA) models show strong performance for data-driven autonomous driving using latest AI technology. As VLA systems combine LLMs for text input, computer vision encoder and complex action generation, the system often consume considerable resources and memory. A recent line of research is focusing on more efficient VLA models, mainly by optimizing how the different modules VLAs interact.

This master’s thesis focuses on evaluation, benchmarking and enhancing of VLA models which focus on efficiency The project involves systematic evaluation of different VLA systems and optimization techniques on realistic benchmarks.

Key Research Areas

• Review of VLA models for autonomous driving regarding efficiency
• Quantitative evaluation of VLA systems and features regarding inference efficiency (time, memory)
• (optional) Enhancements of VLA systems for inference, integrating or combining different mechanisms for efficiency improvement

This position is only for students enrolled in a university looking for a Master Thesis. For students at TUM in München, thesis supervision from university side can be provided. For others, this needs to be agreed with their home university.

Requirements

• Currently enrolled in a computer science / engineering Master studies with a focus on data analysis, automotive, engineering or similar
• Interest in autonomous driving and machine learning
• Basic understanding of deep learning and model training
• Ability to read and modify research codebases
• Proficiency in Python
• Experience with VLA is a plus

Benefits

• Home office: Possibility to work from home
• Health: Yearly health & wellbeing days, Urban Sports Club membership
• Attractive working environment: Modern offices, canteen onsite and free parking
• Open company culture: Diverse, multicultural team
• Events:Regular company & team events