Automotive Production Engineering & Artificial Intelligence (M. Eng.)

Empower engineers to master programming and AI → Learn to design and successfully execute AI projects
Practice-first approach to learning → Up to 80% hands-on experience

The "Automotive and Production Engineering & AI" degree program provides comprehensive specialist knowledge in production and artificial intelligence, two key skills for a successful career in the automotive industry. In an industry that is investing heavily in new technologies, well-trained specialists are in high demand. In addition, the automotive industry is a global market in which international communication and cooperation are crucial. The English-language degree program provides the ideal basis for acquiring these skills and positioning yourself optimally for global competition.

What makes this program different

Master AI & Programming: Learn with real industry tools, not just theory
Real Projects from Day One: Work on automation, data & AI use cases
Up to 80% Hands-On Learning: Practice-first approach with real implementation
Engineering meets AI: Apply AI directly in production & engineering
Learn by Building: Projects, case studies & self-driven development

Technologies You Will Work With

AI & Machine Learning: Python, Anaconda, Jupyter Notebook, ML libraries (e.g. scikit-learn, TensorFlow, Keras) & gen AI: ChatGPT, Claude, Gemini
Automation & Workflows: Power Automate, UiPath, Make, n8n, APIs, RPA
Data Engineering: SQL, PostgreSQL, Orchestration tools (e.g. AirFlow), Containerization (e.g. Docker)

Degree
Master of Engineering (M. Eng.)
Duration
4 semesters (depending on accreditation of gained competencies)
Start of studies
Summer & Winter

Accreditation
in progress
Main teaching language
English

Study fee
Per semester 2.975 EUR plus 82 € Student services fee/semester
Location
Online and TH Ingolstadt

3 Questions for Our Program Director, Prof. Axmann

Curriculum

Module Descriptions

Click on the course title for more information

1. Semester

Credit Transfer based on work experience
15 ECTS

2. Semester

Modern Manufacturing Technologies
5 ECTS

This module covers modern manufacturing technologies such as additive manufacturing, laser technology, and fiber-reinforced plastics. In addition, Python is used to generate CNC machine tool data for simulation purposes in predictive maintenance.

Software used:

AI-assisted programming with Python & Power Automate

Data Science & AI
5 ECTS

This module covers the fundamentals of artificial intelligence and data science, as well as key application areas such as conversational and generative AI. It also addresses automated machine learning, business applications, and ethical and legal considerations.

Software used:

AI application development platforms (e.g., IBM Watsonx portfolio)
General-purpose AI (e.g., ChatGPT, Claude, Gemini)

Scientific Seminar
5 ECTS

This module promotes independent and methodical academic work on current topics in production and digitization. Students work in small groups of two to three.

Software used:

Select any digital technology or software for evaluation

3. Semester

Digital Factory & Digital Eng.
5 ECTS

This module provides an introduction to artificial intelligence and an overview of software applications in industry. It focuses on the evaluation of digital technologies and the practical implementation of automation solutions using low-code and RPA.

Software used for practical projects:

Make or
Power Automate or
UiPath or
N8N

Production System & Plant Design
5 ECTS

This module covers the fundamentals of production systems, process-oriented approaches, and lean manufacturing. It also addresses machine tools, capacity planning, and methods such as MTM and REFA, as well as Design for Manufacturing and Assembly.

Software used:

Halocline VR Planning, Assistant

Data Science & AI II
5 ECTS

This module delves into machine learning approaches, including classical and deep learning methods, and covers the data science workflow. It also addresses the customization of large language models (LLMs), such as through retrieval-augmented generation, as well as the development and prototyping of use cases. Additionally, the module distinguishes between predictive and causal data analysis.

Software used:

Anaconda, Python, Jupyter Notebook, ML libraries (e.g., scikit-learn, TensorFlow, Keras)
Coding assistants
AI Application Development Platforms

4. Semester

Automation & Equipment
5 ECTS

This module covers fundamentals of automation and equipment engineering, including PLC programming in industrial manufacturing, an overview of robotics applications, robot types and kinematics, and basics of vectors and matrices. It also addresses robot programming methods (online/offline) and fundamentals of robotic perception, including CNN-based visual data processing.

Software used:

Automation Software Automation: Siemens TIA Portal, Factory IO
Robot Software: Polyscope (Universal Robots), Phyton
Robot Control: KUKA KRC4

Data Engineering & Databases
5 ECTS

This module covers the fundamentals of big data as well as various data types and structures. Topics include relational and NoSQL database systems, optimized storage formats, distributed file systems, and computing frameworks.

Software used:

SQL, Python
Databases (e.g., Postgres)
Orchestration tools (e.g., AirFlow)
Containerization (e.g., Docker)

Engineering Processes in Automotive Industry
5 ECTS

This module addresses engineering processes in the automotive industry, including product and process development, requirements and quality management methods, and pre-series processes and systems engineering. It also covers modeling with SysML and SPICE for assessing process capability, maturity levels, and performance indicators.

Software used:

Engineering: Cameo or CATIA Magic
SPICE: Polarion, DOORS, Tessy, etc.

5. Semester

Master Thesis
30 ECTS

The master's thesis teaches students how to conduct independent academic research, from defining the problem and conducting research to selecting methods, analyzing data, and presenting results. The focus is on a systematic approach, logical reasoning, and goal-oriented work.

Software used:

Select any digital technology or software for evaluation or application

Quick Info

a) Proof of successful completion of a degree program in engineering, natural sciences, technology or business administration, or
computer science program at a German university with at least 210 ECTS credit points or an equivalent level of study or an equivalent
successful domestic or foreign degree,
b) proof of at least one year of relevantly qualified practical professional experience after completing the university degree or equivalent qualification referred to in a); relevantly qualified practical professional experience is particularly in the areas of product or technology development, information technology (IT) in general, enterprise resource planning (ERP), Industry 4.0, Internet of Things (IoT) or manufacturing execution system (MES); project work in the area of "digitization of the company" is also considered relevantly qualified practical professional experience and
c) proof of sufficient knowledge of the English language (language level B2 of the Common European Framework of Reference for Languages)

The degree program is for "young professionals"

•    with a bachelor’s degree in engineering, natural sciences, computer science or business.
•    who have an affinity for technology and/or digitalization topics
•    with relevant, qualified professional experience of at least 1 year
•    who live outside Germany but have a connection to Germany, e.g. work in a German company
•    who live in Germany and are specifically looking for an internationally oriented, interdisciplinary program with an online and hybrid format

The degree program is for "young professionals"

apply online

Programme Director

Programme director, Academic advisor Automotive Production Engineering (Master)
Prof. Dr. Bernhard Axmann
Phone: +49 841 9348-3505
Room: N108
E-Mail: Bernhard.Axmann@thi.de