Master Thesis Multi-Task Optimization and Deployment of Deep Neural Full-time

SOME IT WORKS. SOME CHANGES WHAT’S POSSIBLE.

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More than 90% of automotive innovations are based on electronics and software. That’s why creative freedom and lateral thinking are so important in the pursuit of truly novel solutions. That’s why our experts will treat you as part of the team from day one, encourage you to bring your own ideas to the table – and give you the opportunity to really show what you can do.

We, the BMW Group, offer you an interesting and varied Master thesis in the area of efficient Hardware-aware Multi-Task Learning.

Deep neural networks (DNNs) have conventionally focused on optimizing for individual task performance, however, the necessity for jointly optimizing multiple tasks and deploying these models on resource-constrained edge devices is rapidly escalating. This master thesis investigates the efficient deployment of DNNs on edge devices, focusing on managing multiple concurrent tasks. The primary objective is to develop an integrated framework that addresses the multifaceted challenges of multi-task optimization and resource constraints, enabling sustainable and high-performance operation of edge AI applications. Extensive experimental evaluation and rigorous analysis will be conducted to validate the effectiveness of the proposed solution, contributing to the field of DNN optimization for edge devices.

What awaits you?

• Literature survey of state-of-the-art methods in multi-task learning and efficient deployment of DNNs on edge devices.
• Experience in implementing novel methods for multi-task optimization and handling gradient conflicts, contributing to our PyTorch-based research stack.
• Engagement in a diverse team with experience in publishing at international peer-reviewed conferences.
• Scientific writing of your thesis and presentation of the research results both at the university and industry.

Please note that your thesis must be supervised by a university on your part.

What should you bring along?

• Educational Background: Currently pursuing a master’s degree in electrical engineering, computer science or a comparable qualification.
• Technical Skills: Knowledge in multi-task learning, resource allocation, model compression, and edge computing. Proficiency in Python, PyTorch, Docker, and Git.
• Practical Experience: Familiarity with deploying deep neural networks on edge hardware.
• Personal Attributes: Highly motivated and eager to collaborate in a team.
• Language Proficiency: Business-fluent English, both written and verbal.

What do we offer?

• Comprehensive mentoring & onboarding.
• Personal & professional development.
• Flexible working hours.
• Digital offers & mobile working.
• Attractive remuneration.
• Apartment offers for students (subject to availability & only Munich).
• And many other benefits – see bmw.jobs/benefits

You are enthused by new technologies and an innovative environment? Apply now!

At the BMW Group, we see diversity and inclusion in all its dimensions as a strength for our teams. Equal opportunities are a particular concern for us, and the equal treatment of applicants and employees is a fundamental principle of our corporate policy. That is why our recruiting decisions are also based on personality, experience and skills.

Find out more about diversity at the BMW Group at bmwgroup.jobs/diversity

Earliest starting date: from 10/01/2024

Duration: 6 months

Working hours: Full-time

Contact:
BMW Group HR Team
+49 89 382-17001