In parallel to conducting state-of-the-art research BIFOLD focuses on educating future generations of data management and machine learning scientists. With a newly established Graduate School and a related international Master’s program (EIT Digital Data Science Master’s) BIFOLD offers excellent training capacities for future researchers.
In addition BIFOLD researchers have established the Data Science and Engineering (DSE) Track at TU Berlin. It enables master’s students in the computer science and information systems management programs to specialize in DSE.
BIFOLD Graduate School
The Graduate School educates students for the rising worldwide demand for specialists with expertise in data management (DM) and machine learning (ML). It is highly research-focused and strives to build the next generation of curious and creative data science experts, who challenge assumptions, find answers to significant questions, and exercise ethical responsibility.
Based on a highly competitive application process, the BIFOLD Graduate School offers an innovative fast-track PhD Program for students holding a bachelor’s degree, as well as a PhD Program for students with a master’s degree.
Each student is individually supervised by a thesis advisory committee (TAC), which supports the student to build and enhance their research skills in a customized way. Key element of the Graduate School is a thematically broad offer of courses and trainings. These include courses relevant to the research carried out at BIFOLD, as well as courses that help the students extend their individual skills. A large range of courses is held by the Research Group Leads and Fellows, thereby tightening the link between education and research. Complementary, the Graduate School offers training in scientific methods and transferable skills, covering for instance: research ethics & management, communication & networking skills and personal effectiveness. In addition, the students have the opportunity to participate in courses at TU Berlin and other Berlin universities.
The annual Graduate School Retreat brings together all PhD students and supervisors for a productive exchange, and to foster communication and collaboration within BIFOLD. At the annual Summer or Winter School members of BIFOLD, invited external experts as well as invited external PhD students come together with all BIFOLD PhD students, to promote a national and international research network around the relevant topics.
BIFOLD students are strongly encouraged to actively take part in national and international conferences. The Graduate School financially supports the establishment of collaborative projects for PhD students, enabling joint project work and research stays at relevant national or international labs.
More Information on Courses
More Information on the Qualification Process
First Cohort of Students
Research project: “Explaining Artificial Neural Network Predictions: Extension and Evaluation”
Advisor: Wojciech Samek
Research project: “Federated Healthcare Analytics”
Advisor: Jorge-Arnulfo Quiané-Ruiz
Research project: “Towards a better understanding of Deep Neural Networks”
Advisor: Marina Marie-Claire Höhne
Research project: “Towards Efficient EO Processing”
Advisor: Volker Markl
Research project: “Linking Tissue Morphologies to Driver Mutations in Non-small-cell Lung Cancer”
Advisor: Grégoire Montavon
Research project: “Generating Molecular Structures with Deep Neural Networks”
Advisor: Kristof T. Schütt
Research project: “Improving Multi-body Sampling Algorithms with Machine Learning Methods”
Advisor: Frank Noé
Research project: “Data Processing in a Fog/Cloud Environment”
Advisor: Steffen Zeuch
Research project: “Theoretical Analysis of Learning Multiple Problems”
Advisor: Klaus-Robert Müller
Research project: “Advanced Scalable and Accurate Multi-Modal/Cross-Modal Hashing Methods for Remote Sensing Image Retrieval from Large Archives”
Advisor: Begüm Demir
Research project: “A New Approach for Transcriptomic Data Analysis based on Dictionary Learning”
Advisor: Tim Conrad
Research project: “Adaptive Monitoring and Fault Tolerance for Distributed Analytics Pipelines”
Advisor: Odej Kao
Graduate School Coordination
Dr. Manon Grube
Technische Universität Berlin
Machine Learning Group