The objective of the PhD program is the joint method training of PhD students in the intersection between algorithmic and mathematical foundations of statistical data analysis and machine learning on the one hand and the system-oriented foundations of databases, computer networks and high performance computing on the other hand. Doctoral students should thus acquire the basics for the entire "Big Data Pipeline", from data generation, storage and processing to application-specific analysis of the data and visualization of the results.   

  • Strengthening interdisciplinary cooperation in the Big Data foundations in mathematics and computer science and close exchange with related disciplines, in particular biology.
  • Creation of a common platform for doctoral students from different faculties and departments with the aim of improving subject socialization and further education.
  • Exchange of experience through joint seminar series and annual retreats.
  • Creation of an internationally competitive research environment that takes into account the rapid developments in the fields of statistical data analysis, machine learning and distributed systems.
  • Joint supervision of doctoral students by highly qualified experts from various disciplines.

At least 18 credit points must be earned as part of the doctoral programme.
The curriculum structured consists of three complementary modules:

Statistic Data Analysis and Machine Learning
  • Computergraphik (6 ECTS)
  • Foundations of Artificial Intelligence (8 ECTS)
  • Iterative Verfahren der Numerik (4 ECTS)
  • Machine Learning (8 ECTS)
  • Pattern Recognition (8 ECTS)
  • Numerik der partiellen Differentialgleichungen (8 ECTS)
  • Planning and Optimization (8 ECTS)
  • Probabilistic Shape Modelling (6 ECTS)
  • Projekt: Inverse problems: numerical and computational aspects (3 ECTS)
  • Seminar: Inverse problems: numerical and computational aspects (3 ECTS)
  • Seminar: Machine Intelligence (6 ECTS)
  • Seminar: Numerik (1 ECTS)
  • Seminar: Wahrscheinlichkeitstheorie (1 ECTS)
  • Wahrscheinlichkeitstheorie (3+4 ECTS)
  • Information Theory (8 ECTS)
Distributed Systems 
  • Computer Networks (4 ECTS)
  • Databases (8 ECTS)
  • Distributed Information Systems (4 ECTS)
  • Foundations of Distributed Systems (8 ECTS)
  • High Performance Computing (4 ECTS)
  • Introduction to Internet and Security (8 ECTS)
  • Multimedia Retrieval (6 ECTS)
  • Seminar Distributed Systems (6 ECTS)
  • Algorithms and Data Structures (8 ECTS)
  • Computer Architecture and Operating Systems (8 ECTS)
    Scientific Methods and Applications
    • Scientific Writing (6 KP)
    • 101 Things I learned in Computer Science (4 KP)
    • Bioinformatics Algorithms (4 ECTS)