When Computers Understand Language: Work with modern AI, develop applications such as chatbots or translators, and understand how machines analyze and generate text.

Make Data Understandable: Analyze requirements, develop interactive visualizations, and apply appropriate visualization techniques to communicate data effectively.

From a complex Problem to an Solution: Work with interdisciplinary teams on real-world, complex challenges, apply Design Thinking, develop user-centered solution ideas, and learn how to turn ideas into compelling concepts—from problem analysis to persuasive presentations.

Develop an innovative solutions for a real world problem: Work in interdisciplinary teams on open-ended challenges, develop user-centered concepts using design thinking, build prototypes in a makerspace, and present your solutions to clients and a larger audience.

You have an idea, which you want to develop further to a digital or physical products? Then experiment with different approaches, learn methods for digital fabrication and present a functional prototype as solution.

Compute, Simulate, and Solve Real-World Problems: Learn the Julia programming language and the fundamentals of scientific computing, including numerical methods and the handling of rounding errors. Then develop your own simulations and applications for engineering, healthcare, and business while gaining insight into modern computational methods and their practical implementation.

Develop Autonomous Mobile Robots: Understand and integrate the subsystems of autonomous robots, apply concepts such as kinematics, sensor fusion, and control engineering, and develop your own solutions for motion and path planning in hands-on research projects.

Design Successful Software Projects: Elicit, analyze, and document requirements for complex software projects, translate customer needs into clear specifications, and use modern tools and language models to improve the quality and efficiency of the development process.

Operate Modern Software Platforms: Develop microservice-based applications, containerize them with Docker, and orchestrate their deployment using Kubernetes. Apply DevOps practices to efficiently develop, deploy, and scale software.

Design Domain-Specific Languages: Analyze domain-specific languages (DSLs) in medical informatics and develop your own DSLs by applying concepts such as parsing, syntax trees, and execution logic.

Analyze Data with R: Develop scripts and notebooks in R, apply statistical methods and machine learning techniques to biomedical and business datasets, and validate hypotheses using data-driven analyses.

Understand the Limits of Computation: Explore computability, decidability, and computational complexity theory while analyzing fundamental questions in computer science—from Turing machines and the Halting Problem to NP-completeness and the P vs. NP problem. Learn to recognize and classify the fundamental limits of algorithmic problem solving.

Study Software and its Development Systematically: Plan and evaluate empirical studies in software engineering, formulate hypotheses, collect and analyze data, and critically interpret research findings using sound scientific methods.

Create a Workshop for your Fellow Master Students: Independently explore a new topic in computer science, collaborate with your team to design a workshop, and teach both theoretical and practical aspects to your fellow students.

Analyze Clinical Data with Machine Learning: Learn how to analyze large collections of electronic patient data using statistical and machine learning methods, apply R for implementation, and interpret the results to generate new medical insights.

Protect Information with Cryptography: Deepen your understanding of modern cryptographic methods and protocols—from digital signatures and zero-knowledge proofs to elliptic curve cryptography and post-quantum cryptography—and learn to evaluate their security and applications.

Improve Patient Care with Intelligent Decision Support Systems: Develop Clinical Decision Support (CDS) functions for electronic health records, learn how medical knowledge can be processed automatically, and evaluate the benefits, limitations, and practical applications of modern clinical decision support systems.

Develop Mobile Apps Professionally: Plan, design, and implement mobile applications—from requirements analysis and UX design through prototyping, architecture, security, testing, and deployment.

Dive into how modern AI works: You will develop a deep understanding of neural network methodologies – from feedforward and convolutional networks to transformers, large language models and generative models – including their mathematical foundations and typical application areas. You also learn how to implement, train and apply these methods in practice using a deep learning framework such as PyTorch.

Tackle real-world challenges in medical data science: You independently develop data-driven solutions for a problem in healthcare - from data preparation and method selection through implementation and evaluation to the presentation of your results.

Develop software in a real-world project environment: Collaborate with an external partner, analyze and structure requirements and develop prototypes for a real-world task. You will organize your team independently and manage the entire development process using an iterative, quality-focused approach.

Develop software in a real-world project environment: Collaborate with an external partner, evaluate various technologies to solve his requirements, design and document a respective software architecture, and develop an executable prototype.

Understand What Users Really Need: Deepen your knowledge of requirements engineering—from requirements elicitation and prioritization to documentation and quality assurance—and develop precise specifications for complex software projects.

Design the Architecture of Complex Software Systems: Explore modern software architectures, analyze their impact on quality attributes, and learn how to systematically model and evaluate architectural concepts.

Understand software and business processes: Learn how software development and business processes are modeled, managed, and improved. You will explore software lifecycle models, traditional and agile development approaches, process selection, and methods for analyzing and optimizing workflows to support efficient and high-quality software development.

Ensure Software Quality Systematically: Learn how to define quality objectives, analyze and improve development processes, and apply quality assurance methods ranging from metrics and reviews to test management and audits.

Turn technological breakthroughs into Innovative solutions with societal benefit: Work in interdisciplinary teams on real-world challenges, explore emerging technologies, and develop innovative concepts that connect scientific advances with societal needs.

Prepare for Your Master`s Thesis: Learn scientific methods, models, and ways of thinking, and apply them to systematically investigate IT-related problems, evaluate research studies, and plan and conduct your own research according to academic standards.

From Idea to Innovation: Work with international teams on real-world challenges, apply Design Thinking, develop user-centered solutions, and learn how to turn ideas into compelling concepts—from problem analysis to persuasive solution presentation.

Write and Defend Your Master`s Thesis: Independently investigate a scientific research question, develop and evaluate your own solutions, and present and defend your results in a final colloquium.