The Helmholtz Einstein International Berlin Research School in Data Science (HEIBRiDS) invites applications for Ph.D. fellowships in a collaborative research environment across disciplinary borders. HEIBRiDS projects combine Data Science with applications in Molecular Medicine, Materials & Energy, Earth & Environment, Geosciences, and Space & Planetary Research and topics belong to one or more of the following areas:
- Machine Learning & Deep Learning,
- Data Management,
- High-throughput Data Analytics,
- Mathematical Modelling,
The research will be conducted at one of the partner Helmholtz Centers in the Berlin region:
or the participating Einstein Center Digital Future (ECDF) partner universities:
If you enjoy working in an interdisciplinary environment, producing cutting-edge research, come and join our international community in Berlin, Germany!
- Cutting-edge research in data science under close supervision of a team of two mentors,
- Three-year fully-funded fellowships (E13 TVöD or TV-L), with a possibility of one-year contract extension,
- The integrated training curriculum of scientific and professional skills courses taught in English,
- Financial support for conferences and collaborations.
To apply, a master’s degree in quantitative sciences (Computer Science, Physics, Statistics or Mathematics) or related applied field (Bio- or Geoinformatics etc.), awarded by July 2020, is required.
Apply by February 7, 2020, via the online HEIBRiDS application portal. Please submit your CV, academic record, contacts of two referees, and motivation letter referring to one or two of the available doctoral projects. A detailed description of the available doctoral projects can be found here upon registration to the application portal.
Successful candidates will start their PhD between July and October 2020.
For more information, visit our webpage: https://www.heibrids.berlin/admission/open-phd-positions/
We adhere to principles of responsible research, good scientific practice, openness, and transparency, and are committed to setting a good example as a scientific environment where scientists can flourish and grow, independently of their national or ethnic background, functional variation, sex/gender identity/alignment/orientation, family configuration, or other such feature of their persons or contexts.
Severely disabled persons will be given preferential treatment in the case of equal qualification.