Teaching and continuing education

These three mathematical sub-disciplines form the cornerstone of every technical and scientific education. They provide the methodological skills for structuring, formulating and solving complex problems. In our courses, we teach not only the technical content but also the mathematical-analytical way of thinking. This enables our students to examine problems of all kinds systematically and logically and to solve them efficiently and effectively. The approach involves recognizing patterns and processes, understanding abstract concepts and applying mathematical principles to arrive at well-founded conclusions. Concrete questions are converted into a mathematical form in order to subsequently process them graphically, analytically or numerically with computer support.

Today, mathematical calculation models are capable of realistically mapping the behavior of many real systems and processes as a digital twin. This makes it possible to predict and analyze the behavior of such systems under various conditions before they are physically implemented. These simulations enable virtual experiments, design studies and sensitivity analyses. They thus extend the development process and enable optimizations and insights that would not be achievable with a purely empirical development strategy. In this way, development loops can be shortened and the realized prototypes already have a high degree of maturity. Our students learn methods and procedures for the creation and implementation of such models as well as their simulation, evaluation and optimization, which can be used profitably in numerous areas of application.

We live in the information and data age. Knowledge and data are more easily and comprehensively available than ever before. However, this poses the challenge of gaining a quick and meaningful overview of available data. Statistics is the appropriate tool for processing, structuring, condensing and visualizing data and ultimately deriving insights and predictions from it. It uses a wide range of methods, from simple frequency analyses to complex statistical test procedures and regression models. The aim of statistics and data analysis is to extract useful information from collected data, recognize patterns and correlations and ultimately make well-founded decisions. We enable our students to usefully handle data and information by teaching basic concepts and statistical methods using practical data sets.

Based on advanced methods of statistics and computer technology, it is now possible to create universal prediction models with previously unimaginable performance. Thanks to this technology, computers are able to learn independently from data and recognize patterns. These machine learning models enable the automation of processes and actions that were previously the exclusive domain of (highly trained) humans. Artificial intelligence even enables autonomous decision-making and the independent control of processes. The applications of machine learning and AI are diverse and range from personalized recommendations in online stores to medical diagnoses and the optimization of process chains. We familiarize our students with the fundamentals and applications of this cutting-edge, rapidly developing technology, which is currently transforming our society, economy and science.

Data engineering deals with the systematic collection, storage and processing of large amounts of data and is of central importance in our modern, data-driven world. It involves the development and maintenance of infrastructures and tools that enable data to be collected, transformed and analyzed efficiently. Our students learn to create scalable data pipelines, convert raw data from various sources into usable formats and make it accessible for analysis and machine learning. We also raise awareness of data security and data infrastructure management. These skills are essential as they form the basis for data-driven decisions in companies and research.

Our physics courses familiarize students with fundamental phenomena from the fields of mechanics, electromagnetism, thermodynamics, vibrations/waves and optics, which serve as a basis for more advanced technical courses in all engineering disciplines. The courses, which are designed as experimental lectures, mainly follow an inductive learning approach based on observations using numerous experiments and the findings derived from them. This trains the ability to develop an understanding of relationships from precise observations and then to describe them (mathematically). This (natural) scientific ability to deal analytically with complex relationships is invaluable in our highly complex working and living environment.