HEIBRiDS - Doctoral Program in Data Science

Established in 2018, HEIBRiDS is a doctoral research school in Data Science.  In cooperation between 6 Helmholtz-Centers (AWI, DESY, DLR, GfZ, HZB and MDC) located in and around Berlin and the Einstein Center for Digital Future (ECDF) involving the Berlin univerisities Charité, FU, HU, TU, and UdK, HEIBRiDS benefits from interdisciplinary projects, joint supervision and mentorship from excellent researchers, affiliation of every doctoral candidate with two centers of teaching and training excellence, extensive research training and professional development program opportunities, and dedicated funding for participation in international conferences and collaborative visits.


The research focus of HEIBRiDS exploits the primary expertise of the participating members representing different domains and scientific disciplines. The Helmholtz centers have first-class researchers from the domains medicine, transportation, earth sciences and, climate at their disposal. The ECDF hosts researchers from the digitalization core technologies, from digital health to digital industry to digital humanities. Such a consortium creates a unique environment, as it allows investigation of core methodologies, algorithms, and processes from different angles and transfer knowledge between scientific disciplines.

Data Science with emphasis on
Machine Learning
Novel Hardware Concepts
HEIBRiDS focuses on data science from medicine and geo-sciences to information technology and engineering and provides an internationally highly recognized and vivid research environment within these research areas combined with the supervision by an interdisciplinary team and a rich lecture program.

For the 2018 intake HEIBRiDS will offer 10-15 PhD positions for outstanding students holding a master’s degree in quantitative sciences or related subjects. The ideal applicant has experience in the general research area of HEIBRiDS and enjoys working in an interdisciplinary environment. All students are required to meet the regular admission requirements of the Berlin universities' doctoral programs. As HEIBRiDS aims at interdisciplinary research there is no complete list of master’s degrees eligible for the program and the selection process will not solely consider the grades, but the candidates specialist knowledge in the proposed research area.
Duration of support: 3 years with the option of 1 year extension

Application deadline:  Mar 3, 2018

The doctoral training will offer a framework program with following components:

1. PhD topics will be proposed by a team of two professors – one from Helmholtz and one from ECDF – describing an interdisciplinary, data-science topic and define the main research questions with respect to the current state-of-the art.

2. Research curriculum to impart the necessary knowledge with domain-specific courses for computer/data scientists and data science methods for computer/domain-scientists.

3. Soft-skills with typical courses on presenting, self- and project management, communication in interdisciplinary and international teams as well as responsible involvement in organizing interdisciplinary skills.

4. Experimental work (lab skills) with tutorials for large devices (microscopes, HPC, …) as well as writing scientific proposals and involvement in collaborative projects.

The research curriculum will be provided by the ECDF using the courses of the participating universities and the Charité. The courses for soft-skills are already provided by each institution, so the enrolled PhD students will participate in the programs of the university, where the primary supervisor is affiliated. In addition, the school itself will offer a wide range of events, such as round-table lectures, workshops, theme and methods days, etc.

The governance of the school includes two spokespersons, one from the Helmholtz Association and one from the ECDF, a steering committee with a member from each participating Helmholtz Center/ ECDF university, and an international scientific advisory board with two renowned experts per scientific domain.