This master’s degree trains you in the mathematical foundations and algorithmic techniques that underpin contemporary applications in artificial intelligence, digital communications, e‑commerce, medical technology and social networks. The programme treats applied mathematics as a technology‑driven, algorithmic discipline, linking rigorous mathematical problems with practical, industrially relevant solutions. It is aimed at students who want to contribute to advances in machine learning, network analysis and data sciences and to pursue either academic research or technical careers in these areas.
The curriculum emphasizes applied mathematics for machine learning, statistical data analysis and mathematical modelling, while also offering modules on cryptography/cryptanalysis and graph theory relevant to modern network and data sciences. Core taught content includes machine learning, cryptography and cryptanalysis, advanced graph theory, simulation and visualisation, and modern analysis for statistical learning theory. Practical skills—particularly programming and presentation—are developed through project work, and students can select additional courses from a broad catalogue spanning pure mathematics and application domains. Faculty research areas include machine learning, several branches of discrete mathematics, cryptography, and stochastic processes in biomathematics; students are encouraged to join these activities and may choose their own thesis topic within these fields.
The programme integrates hands‑on experience with academic study: a five‑month internship in industry or university research allows you to apply theoretical knowledge in practice and can feed directly into your master’s thesis. The thesis itself runs for five months and concludes with a colloquium; regular attendance is not required during this period. All courses are taught in English, and the master’s thesis may be written in either English or German. Graduates leave prepared for careers in computational intelligence, data science, network engineering and related research areas.
Programme requirements and structure (key facts)
This program develops mathematical and computational expertise tailored to networks and data-driven problems. In the first year you build foundations in machine learning and cryptography while strengthening analysis skills; core modules include Computational Intelligence and Machine Learning I, Foundations of Modern Cryptography, and Topics in Modern Analysis. Courses combine lectures, seminars and laboratory components, so you’ll learn both theory and practical implementation from the outset.
In the second semester you continue to advanced applied topics such as Computational Intelligence and Machine Learning II, Simulation and Visualisation, Cryptanalysis, and Advanced Graph Theory and Network Algorithms. These modules emphasize algorithm design, secure and adversarial aspects of data systems, and methods for modelling, simulating and visually exploring complex datasets and networks. A range of elective courses lets you tailor the programme toward computational mathematics, statistics, discrete mathematics, signal processing, computer science topics, or digital video analysis.
The final year is research-focused: you complete an internship (either in industry or university research) and participate in a research seminar in the third semester, then carry out an independent Master’s thesis project and colloquium in the fourth semester. Expected outcomes include the ability to design and analyze algorithms for networked systems, apply and evaluate modern machine learning methods, perform cryptographic and cryptanalytic reasoning, run simulation and visualization workflows, and conduct original applied research.
Program structure & requirements (concise)
This master's programme is aimed at applicants who already have a university-level qualification in mathematics. Eligible candidates must hold a first professional degree in mathematics with a minimum standard study period of six semesters (typically a Bachelor's degree or a diploma), or hold a degree that the university recognises as equivalent.
Applicants should therefore have a solid mathematical background. If you hold an international qualification and are unsure whether it meets the equivalence criteria, contact the admissions office or review the university’s recognition guidelines for clarification.
Winter Semester (International)
15 May 2026
Winter Semester (EU/EEA)
15 May 2026
Graduates are prepared for careers that require deep mathematical and algorithmic expertise in data‑driven fields: roles such as data scientist, machine learning engineer, cryptography/security analyst, network analyst or quantitative researcher in industry and research institutions. The combination of theory, applied projects and a research internship equips you to work in technology companies, research labs, financial services, healthcare analytics, or pursue doctoral studies.
The programme’s emphasis on practical labs, simulation and an extended research internship makes it particularly suitable for students aiming to transition directly into applied R&D positions or collaborative projects between academia and industry.