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Digital Engineering

 LATEST: Slides from the Digital Engineering introductory session, October 2026:

 

Logo Digital Engineering

The Departments of Electrical Engineering , Computer Science, and Mechanical Engineering at the School of Science and Technology at the University of Siegen have been offering the following three innovative degree programs as bachelor’s degrees (7 semesters) since the 2024–25 winter semester and as master’s degrees (3 semesters) since the 2026 summer semester:

Logo 3 Digital Engineering Studiengänge

 

The goal of these degree programs is to integrate engineering and computer science. They combine the essential elements of an engineering program with practice-oriented components of computer science, particularly software engineering and machine learning. 
Digital Engineering offers challenging degree programs that address the key challenge of future industrial value creation: the integration of traditional engineering tasks and software. 
This flyer summarizes the key points of the new “Digital Engineering” degree programs. 

You can find the most important information in the FAQs (PDF):


 

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Logos Unterstützer Digital Engineering

More information (PDF):

 

The three degree programs have different focuses on the various engineering disciplines (listed in credit points):

Tabelle Leitungspunkte Digital Engineering

 

Tortendiagramm Digital Engineering (fachl. Zusammensetzung)

 

A more detailed list of the courses in the three degree programs: 

Detaillierte Fä#cherdarstellung Digital Engineering
 
The mathematics component is nearly identical across the three degree programs: 
  • Advanced Mathematics I and II
  • Discrete Mathematics
  • Numerical Methods or Advanced Mathematics III
and the computer science component is identical across the three degree programs:
  • Digital Technology
  • Algorithms and Data Structures
  • Object-Oriented and Functional Programming
  • Programming Practicum
  • Introduction to Machine Learning
The challenge of “software competence” for engineers has now made its way into the press and everyday life. From electric cars to smart meters to Industry 4.0, a lack of software expertise among engineers is currently the bottleneck preventing more innovative products and higher quality. The share of software in value creation is rising steadily and rapidly, meaning that this challenge will gradually affect all areas of our lives. 
 
A second major driver of innovation is “artificial intelligence, ” particularly machine learning. In the engineering field, data-driven models are increasingly being used alongside traditional white-box models(first-principles models) based on the laws of mechanics, electrical engineering, thermodynamics, and fluid dynamics, among others. These so-called black-box models—such as neural networks —are trained using measurement data (or simulation data). This makes it possible to describe many highly complex processes relatively quickly and cost-effectively. This raises many new and important questions in both research and practice regarding robustness, reliability, and interpretability, which require expertise in both computer science and engineering. 
 
Other important areas of application for machine learning in the modern industrial environment include the automated analysis of large amounts of data (big data), known as data mining, image processing, and expert systems based on large language models (ChatGPT) and intelligent search (Google, Bing). 
 

Possible Fields of Work 

  • Development and software implementation of new functions in control units, control systems, monitoring and diagnostic systems, and for virtual sensors, ...
  • Optimization of process automation
  • Robust AI applications in industry
  • Data mining: discovering, utilizing, and visualizing relationships, correlations, patterns, and clusters in large, high-dimensional datasets
  • Cyber-physical systems that integrate mechanical, electrical/electronic, and software components and combine cybernetics with mechatronics
  • Monitoring and predictive maintenance require robust models that combine physical understanding with data-driven learning methods, thereby increasing reliability while simultaneously reducing costs 
  • Efficiency optimization through model-based approaches—the models can be derived from physics (white-box), learned from data (black-box), or generated from a combination of both (gray-box) 
  • Test bench automation and optimization through intelligent design of experiments 
  • Transition from traditional hardware solutions to flexible software implementations 
  • Metamodeling of CAD/CAE designs using neural networks for rapid optimization 
  • Industry 4.0, e.g., networking, cloud integration, big data, encryption, visualization, and interpretation of complex, nonlinear, multivariate relationships 
  • Automation using autonomous vehicles 
  • Integration of modern image and video processing into process automation, particularly quality control 
  • Machine optimization through networking and AI 
  • Processing of cloud data with the goal of optimized control of production processes, e.g., in additive manufacturing (3D printing) 
  • Knowledge Transfer Management: Support for maintenance and repair work using augmented reality, data gloves, and similar technologies 

  

Bachelor’s Degree Program

 

Bachelor’s in Digital Engineering: Modules

Study Plans for All Degree Programs (PDF):

 

Module aller DE Studiengänge

 

Master’s Program

It is recommended to follow the7-semester Bachelor’s program in Digital Engineering with a3-semester Master’s program. This offers the following options:

Bachelor’s Digital Engineering – Mechatronics Digital Engineering – Mechanical Engineering Digital Engineering – Electrical Engineering
 
Master's Mechatronics
(English)
 
Digital Engineering – Mechanical Engineering
(German / English, depending on the choice of modules)
 
Digital Engineering – Electrical Engineering
(German / English, depending on the choice of modules)
 

Further information on the master’s programs (PDF):

 

6-semester Bachelor’s program … What’s next?

Graduates of a 6-semester bachelor’s program in mechanical engineering, automotive engineering, mechatronics, or similar fields also have the option to pursue the Master’s in Digital Engineering – Mechanical Engineering. In such cases, individual requirements equivalent to approximately one semester (30 LP) will be assigned to cover the missing content, typically in the field of computer science. In general, admission to the Master’s program in Digital Engineering – Mechanical Engineering also requires completion of a programming internship worth at least 6 credit points (LP).
 
Bachelor und Master Schema Digital Engineering

 

The following three overviews serve as a guide for possible 4-semester Master’s study plans in Digital Engineering—Mechanical Engineering or Electrical Engineering for graduates of a 6-semester Bachelor’s program. The required courses are outlined in red and fit seamlessly into the program. As is easy to see, these required courses cover a large portion of the computer science content from the Bachelor’s program in Digital Engineering—Mechanical Engineering or Electrical Engineering. This allows graduates of, for example, traditional mechanical engineering, industrial engineering (WIW), or electrical engineering programs to launch their careers in the digital age with this master’s degree. 

Master Digital Engineering Auflagen

 

Master Digital Engineering WIW Auflagen

 

Master Digital Engineering ETI Auflagen

 

Contact: 

Prof. Dr.-Ing. Oliver Nelles