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 systemoriented 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 applicationspecific 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:
Module  Contents 

Statistic Data Analysis and Machine Learning 

Distributed Systems 

Scientific Methods and Applications 
