AI Engineering of Autonomous Systems

Program overview This English-taught Master’s programme prepares you to design, build and deploy intelligent systems that learn, decide and operate autonomously. You will gain the theoretical foundation and practical skills needed to work with state-of-the-art AI technologies that are reshaping industries and everyday life, with a strong emphasis on systems that function without human intervention.

What you will study and how The curriculum covers the technical building blocks of modern autonomous systems: sensor networks and data fusion, data engineering, computing and software engineering, machine learning and decision-making, perception and cognition, and connectivity. Teaching combines hands-on, application-oriented training with theoretical insight, led by faculty who are experts in AI engineering and contributors to the “AImotion” initiative funded by the Bavarian government. Ethical aspects and development challenges of autonomous systems are integrated into the programme.

Graduate outcomes and career relevance By graduation you will be able to design and implement intelligent systems that learn from data, adapt to changing conditions and make decisions to optimise overall performance. The skills you acquire are applicable across many sectors — mobility, manufacturing and production, agriculture and medical engineering — where autonomous systems can add new functionality, improve safety and increase efficiency. The programme emphasizes real-world problem solving and industry-relevant competencies to help you transition into professional or research roles.

Key facts & practical information

• Degree awarded: Master of Arts
• Academic field: Computer Science
• Language of instruction: English
• Core topics: sensor networks and data fusion; data engineering; computing & software engineering; machine learning & decision-making; perception & cognition; connectivity
• Teaching approach: practical, hands-on training complemented by theoretical foundations; faculty involved in the AImotion initiative (funded by the Bavarian government)
• Industry relevance: prepares students for roles in mobility, production, agriculture, medical engineering and related fields
• Admissions and application details: official entry requirements, application deadlines and tuition information are set by the university — check the programme’s web page or contact the admissions office for precise criteria and documentation needed.

Curriculum overview

This three‑semester Master’s programme combines required and elective modules to train engineers in building intelligent, adaptable autonomous systems. Core coursework develops expertise in sensing and sensor fusion, decision‑making and planning, and software architectures for systems that must operate safely and reliably in changing environments. Electives let you specialise in areas such as automotive applications, innovation and engineering processes, and language skills.

Core module focus

• Intelligent Sensing and Exploration teaches how multiple sensors are integrated to form fast, accurate representations of the environment and physical events so decision components can act reliably.
• Planning, Control and Decision Making covers methods for goal‑directed and adaptive behaviour, emphasising planning under uncertainty and using machine learning to combine sensory input with experience for better actions.
• Software Methods and System Development treats autonomous systems from a software engineering viewpoint, including cloud and edge architectures and learning models that enable systems to change behaviour dynamically as operating conditions shift.

Practice, research and outcomes

Hands‑on and research elements complement theory: a research methods module guides students through applying scientific methods to a current AI engineering topic and reporting findings in a seminar paper, while a group project provides collaborative “learning by doing” experience culminating in a project presentation and a summary paper. Graduates leave with practical skills in multi‑sensor integration, intelligent planning and control, software design for adaptive systems, teamwork, and technical communication — abilities directly relevant to roles in autonomous systems development and related industries.

Key modules and learning outcomes

• Intelligent Sensing and Exploration — master sensor integration and real‑time environment modelling (sensor fusion, perception pipelines).
• Planning, Control and Decision Making — design and evaluate planning and control strategies; apply machine learning to improve decision quality and adaptability.
• Software Methods and System Development — engineer cloud/edge architectures and software solutions that allow autonomous systems to adapt at runtime.
• Electives — specialise in automotive technology, innovation management, engineering processes, or improve German language skills.
• Research Methods — conduct a focused scientific investigation and produce a seminar paper; present and discuss results with peers.
• Group Project — develop a team project from concept to presentation and written summary; gain practical experience in collaborative system development.

Graduates are prepared for technical roles designing and implementing AI-driven autonomous systems across sectors such as automotive and mobility, industrial automation, agriculture and medical engineering. Typical positions include AI/ML engineer, autonomous systems developer, systems engineer, perception or sensor-fusion specialist, and software engineer for embedded and edge/cloud architectures.

The programme also provides a foundation for applied research roles or further academic study in AI and robotics, supported by research methods training and opportunities for project collaborations with industry partners.