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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.

  • PhD Research Seminar Series: The students themselves moderate, present and discuss their research.
  • Invited Lecturer Series: The Graduate School aims to bring relevant expertise from around the world to Berlin. Students are invited to make suggestions for a “wish list” of guest speakers.
  • Research Ethics & Management: The course includes legal and social aspects of information technologies, issues related to public display, intellectual property, patent database search, and business plan writing.
  • Communication & Networking Skills: The course includes academic writing, presenting, networking, public outreach, plus language classes in German and English (if needed).
  • Personal Effectiveness: The course includes aspects of professional development such as project management, time management, stress management, and motivation.
  • One-off Workshops: These are largely held by external experts and cover overarching topics relevant to academia, industry and society, such as good scientific practice, gender awareness, critical thinking, and public outreach.
  • Introductory & Advanced Courses on Machine Learning and Data Management:
    The Graduate School provides spaces in introductory as well as advanced and specialized elective courses in and around the areas of machine learning and data management as well as on their overlap. These courses are offered by BIFOLD members, but also by other lecturers from TU Berlin and/or other Berlin universities. Course examples: Management of Data Streams, Scalable Data Science: Systems and Methods, Big Data Analytics, Database Technology, Hot Topics in Information Management Project; Machine Learning 1 & 2 (+ Electives), Machine Learning in Applications, Hot Topics in Machine Learning, Classical Topics in Machine Learning, Python for Machine Learning; Joint Seminar on Machine Learning and Data Management Systems.
  • Individual Research Project (IRP): In addition to the introductory and advanced courses, BIFOLD Research Group Leads offer individualized courses at a one-to-one teacher-to-student level. These encompass the intensive literature-based study of one chosen research question that specifically interests the student and may become the topic of their Master’s and/or PhD thesis.

Bachelor‘s Entry/Fast-Track PhD
Outstanding students with a bachelor degree will be admitted to complete their PhD within five years. The first year’s curriculum will feature basic courses in Data Management, Machine Learning and related areas relevant for the pursuit of a PhD, as well as a customized, individual research-oriented study project (IRP). Students write a master’s thesis, ideally in the form of a scientific publication, thereby obtaining their master’s degree “en-route”. In their second year they are admitted to the skill building courses of PhD year one.

Master’s Entry
Students with a master’s degree will typically complete their PhD within four years: One year of skill building followed by three years of research-focused skill enhancement. The program provides technical courses for scientific study as well as methodological ones.

Besides the qualification program including local and external courses, the Graduate School offers additional support for its students, including professional and career development as well as supervision and course curriculum customized to the student at the individual level with the help of their thesis advisory committee (TAC).

First Cohort of Students

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Leila Arras

Research project: “Explaining Artificial Neural Network Predictions: Extension and Evaluation”

Lennart Behme

Research Associate

Baris Büyüktas

Research project: “Novel Federated Learning Methods for Efficient, Effective and Privacy- Sensitive Earth Observation”

Kirill Bykov

Research project: “Towards a better understanding of Deep Neural Networks”

Gabriel Dernbach

Research project: “Linking Tissue Morphologies to Driver Mutations in Non-small-cell Lung Cancer”

Niklas Gebauer

Research project: “Generating Molecular Structures with Deep Neural Networks”

Leon Klein

Research project: “Improving Multi-body Sampling Algorithms with Machine Learning Methods”

Anastasiia Kozar

Research project: “Data Processing in a Fog/Cloud Environment”

Hannah Marienwald

Research project: “Theoretical Analysis of Learning Multiple Problems”

Rudi Poepsel Lemaitre

Research project: “Compliant Data Processing”

Mona Rams

Research project: “A New Approach for Transcriptomic Data Analysis based on Dictionary Learning”

Dominik Scheinert

Research project: “Adaptive Monitoring and Fault Tolerance for Distributed Analytics Pipelines”

Parastoo Semnani

Research project: “Optimized search strategy for composition of inorganic materials”