Berlin Institute for the Foundation of Learning and Data (BIFOLD)
Natural SciencesMathematicsComputer and information sciencesPhysical sciencesEngineering and TechnologyElectrical engineering, electronic engineering , information engineeringMechanical engineeringChemical engineeringMedical engineeringEnvironmental biotechnologyIndustrial biotechnologyOther engineering and technologiesHealth biotechnology

The Graduate School of the Berlin Institute for the Foundations of Learning and Data (BIFOLD) offers a strongly research-oriented qualification program. Integrated within the range of science at BIFOLD, the program is designed to implement an international, integrated PhD approach that matches and extends the BIFOLD research profile.
The program is designed to impart to the student a deep scientific understanding of the relevant systems, underlying technologies, and algorithmic skills, as well as their use in an application area. Besides their research, doctoral candidates will participate in yearly research retreats, summer/winter schools, BIFOLD seminar series, and complete coursework encompassing transferable skills, such as network building, research management, and improving communications skills, as well as technical courses in data management and machine learning (ML).

Focus

The focus of our interdisciplinary program is the integration of big data processing systems and ML algorithms, to enable students to devise novel artificial intelligence solutions to scientific problems.       
Project topics range from those directly focusing on current data science and ML challenges, e.g. human and technical latencies, robust and interpretable ML methods and XAI, to AI applications in for instance chemistry and physics, earth observation, to the analysis of medical imaging data.          
The goal of our 4-years research-focused program is to provide our students with an excellent education and world-class research environment on big data management systems and machine learning, two fields with mirids of applications and extremely high in demand.

Application:
Deadline: 30.03.2022
Detailed Information: https://tub.stellenticket.de/en/offers/125975/
Application by email to: gsapplication@bifold.tu-berlin.de

Contact Information

Chair:
Prof. Dr. Volker Markl & Prof. Dr. Klaus-Robert Müller
Coordinator:
Dr. Manon Grube & Dr. Tina Schwabe
Telephone:
+49 (0)30
Deadlines:
30.03.2022
Places:
10 per year
Scholarships:
10 per year, for 4 years
Uni:
Technische Universität Berlin