Master Thesis Sample-Efficient Reinforcement Learning for Autonomous Driving (f/m/x)

BMW M Motorsport / BMW Group; München; Vor Ort; EUR 60.000 - 80.000; Vollzeit

Zusammenfassung

A leading automotive company in Munich offers a Master's thesis opportunity focused on autonomous driving. You will engage in innovative research, support the development of AI models, and collaborate with a diverse team. Ideal candidates are studying computer science or a related field, possess strong knowledge of machine learning concepts, and are proficient in Python and PyTorch. Flexible working hours, mentorship, and competitive remuneration are provided.

Leistungen

Comprehensive mentoring & onboarding

Personal & professional development

Flexible working hours

Digital offers & mobile working

Attractive & fair remuneration

Apartment offers for students

Qualifikationen

• Strong knowledge in computer vision concepts, unsupervised machine learning, and state-of-the-art architectures.
• Excellent programming skills in Python and PyTorch; familiarity with Docker and Git is a plus.
• Curiosity and motivation to solve problems and share ideas in a team.

Aufgaben

• Support researching state-of-the-art autonomous driving models and improve upon them.
• Implement a novel architecture or training paradigm, enhancing team’s research.
• Utilize advanced training infrastructure to conduct experiments and evaluate approaches.
• Assist in presenting thesis results using the scientific method.

Kenntnisse

Computer vision concepts

Unsupervised machine learning

Python programming

PyTorch

Docker

Git

Fluent English

Ausbildung

Studies in computer science or related field

Jobbeschreibung

Overview

We, the BMW Group, offer you an interesting and varied Master’s thesis in the field of autonomous driving. Our team is dedicated to researching and advancing cutting-edge technologies in this domain. At the Autonomous Driving Campus in Unterschleißheim, you will support the development of innovative AI models and collaborate closely with doctoral candidates and fellow students in a dynamic research environment.

Responsibilities

• You will support researching state-of-the-art autonomous driving models and help to test and improve upon them.
• Furthermore, you contribute by implementing a novel architecture or training paradigm, enhancing our team’s research.
• In addition, you utilize our advanced training infrastructure to conduct experiments and efficiently evaluate your approach.
• Moreover, you assist in presenting thesis results using the scientific method, both in written and oral formats.
• You will collaborate with an experienced team that publishes at international peer-reviewed conferences.
• Here, you engage with an international and diverse team of doctoral candidates and students at the Autonomous Driving Campus in Unterschleißheim.

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

Qualifications

• Studies in computer science or a related field with focus on machine learning or artificial intelligence.
• Strong knowledge in computer vision concepts, unsupervised machine learning, and state-of-the-art architectures.
• Excellent programming skills in Python and PyTorch; familiarity with Docker and Git is a plus.
• Curiosity and motivation to solve problems and share ideas in a team.
• Fluent English skills.

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

What we offer

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

Earliest starting date: 11/01/2025

Duration: 6 months

Working hours: Full-time

Do you have any questions? Then simply send your enquiry using our contact form. Your enquiry will then be answered by telephone or e-mail.

At the BMW Group, we place great importance on equal treatment and equal opportunities. Our recruiting decisions are based on the personality, experience, and skills of the applicants.

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