Helmholtz Information & Data Science School:

HEIBRiDS

Looking into the depths of the universe or predicting earthquakes – at the Helmholtz Einstein International Berlin Research School in Data Science (HEIBRiDS) the study of data science has a broad horizon.

The Berlin Research School in Data Science

A unique research environment characterizes the Helmholtz Einstein International Berlin Research School in Data Science (HEIBRiDS): Here, research on the core methods, algorithms and processes of digitization is enabled from different perspectives and knowledge is transported between different disciplines.

HEIBRiDS brings together six Helmholtz centers and four university partners from the Einstein Center Digital Future (ECDF), which focuses on core digitization technologies, from digital health to digital industry and the digital humanities. The participating Helmholtz centers have first-class expertise in the fields of molecular medicine, astrophysics, polar and marine research, aerospace, materials science and geosciences.

HEIBRiDS enables us to look at the even bigger picture and truly think outside the box. That’s why I try to look at my topic from a data sciences perspective at every stage, too, and use methods that maybe haven’t been used by anyone else before.

Gregor Pfalz

is a doctoral student at HEIBRiDS. He analyzes data derived from the sediments of Arctic lakes to make climate predictions. Read more about his project

Mission

In an interdisciplinary PhD program, young scientists are trained in data science applications in a broad range of scientific fields.

The goal of HEIBRiDS is to train a new generation of data scientists who understand the requirements and challenges of those disciplines in which data science has become indispensable.

Research Areas

Participants in the HEIBRiDS program are pursuing PhDs in very diverse research areas, ranging from Earth & Environment, Astronomy, Space & Planetary Research to Geosciences, Materials & Energy to Molecular Medicine.

Overview of current PhD projects

 

"I found data science exciting and wanted to learn something new. Computer science, data science and satellites are a very nice combination."

Olga Kondrateva, PhD researcher at HEIBRiDS

Curriculum

  • Supervision: tandem supervision by a university and a Helmholtz partner; biannual meetings with both supervisors; annual meeting with the interdisciplinary Thesis Advisory Committee
  • Courses for the scientific and transferable skills training: Individually designed curriculum according to the respective research profile. Access for all HEIBRiDS doctoral candidates to courses from an extensive range of courses offered by the Berlin University Alliance and the Helmholtz partners, as well as courses specially organized for the doctoral candidates in the program
  • Mandatory participation in the PhD Seminars and the Data Science Lectures, held twice a month 
  • HEIBRiDS Retreat: Presentation of one's own research project and feedback from the program PIs at the annual HEIBRiDS Retreat
  • Participation in (international) conferences

Funding and Duration of the Program

The program will run for four years and offers full funding. The remuneration during the term corresponds to the tariff level E13 of the TVöD or the TV-L (depending on the institution where the employment takes place).

Application and Further Information

A total of 25 doctoral positions are available, all of which have already been filled. The HEIBRiDS location is Berlin, but depending on the disciplinary connections, there are different locations for doctoral students in the Berlin area. Program language is English.

Contact

PD Dr. Eirini Kouskoumvekaki

Our Doctoral Researchers

Siddhant Agarwal

Siddhant Agarwal
Doctoral Researcher HEIBRiDS

Philipp Baumeister

Philipp Baumeister
Doctoral Researcher HEIBRiDS

Ivo Daniel

Ivo Daniel
Doctoral Researcher HEIBRiDS

Felix Fiedler

Felix Fiedler
Doctoral Researcher HEIBRiDS

Femke van Geffen

Femke van Geffen
Doctoral Researcher HEIBRiDS

Binayak Gosh

Binayak Ghosh
Doctoral Researcher HEIBRiDS

Thorren Gimm

Thorren Gimm
Doctoral Researcher HEIBRiDS

Paolo Graniero

Paolo Graniero
Doctoral Researcher HEIBRiDS

Brian Groenke

Brian Groenke
Doctoral Researcher HEIBRiDS

Olga Kondrateva

Olga Kondrateva
Doctoral Researcher HEIBRiDS

Henning Lilienkamp

Henning Lilienkamp
Doctoral Researcher HEIBRiDS

Nicolas Miranda

Nicolas Miranda
Doctoral Researcher HEIBRiDS

Jannes Münchmeyer

Jannes Münchmeyer
Doctoral Researcher HEIBRiDS

Lusinè Nazaretyan

Lusinè Nazaretyan
Doctoral Researcher HEIBRiDS

Gregor Pfalz

Gregor Pfalz
Doctoral Researcher HEIBRiDS

Sergey Redyuk

Sergey Redyuk
Doctoral Researcher HEIBRiDS

Tabea Rettelbach

Tabea Rettelbach
Doctoral Researcher HEIBRiDS

Contact

Tabea Rettelbach
Tabea Rettelbach
Project title: "Facilitating Machine Learning on Super-High Resolution Earth Observation Data for Detecting and Quantifying Arctic Permafrost Thaw Dynamics"
Elizabeth Robertson

Elizabeth Robertson
Doctoral Researcher HEIBRiDS

Mario Sänger

Mario Sänger
Doctoral Researcher HEIBRiDS

Contact

Mario Sänger
Mario Sänger
Project title: "Representation Learning for Corpus-level Biomedical Relation Extraction"

Supervisors:

Ulf Leser (HU)

Sepideh Saran

Sepideh Saran
Doctoral Researcher HEIBRiDS

Contact

Sepideh Saran
Sepideh Saran
Project title: "Machine Learning Methods for Integration and Analysis of Multi-omics Biomedical Data"

Supervisors:

Uwe Ohler (MDC)

Hermann Stolte

Hermann Stolte
Doctoral Researcher HEIBRiDS

Kevin Styp-Rekowski

Kevin Styp-Rekowski
Doctoral Researcher HEIBRiDS

Peter Tillmann

Peter Tillmann
Doctoral Researcher HEIBRiDS

Christian Utama

Christian Utama
Doctoral Researcher HEIBRiDS

Nadja Veigel

Nadja Veigel
Doctoral Researcher HEIBRiDS

Anna Vlot

Anna Vlot
Doctoral Researcher HEIBRiDS

Leon Weber

Leon Weber
Doctoral Researcher HEIBRiDS

Xiaoyan Yu

Xiaoyan Yu
Doctoral Researcher HEIBRiDS

Peter Hirsch

Peter Hirsch
Doctoral Researcher HEIBRiDS

Contact

Peter Hirsch
Peter Hirsch
Project title: "Development and Application of Novel Methods to Analyze Cells and Cell Lineages in a High Throughput Manner"

Supervisors:

Dagmar Kainmueller (MDC)

Oleksii Martynchuk

Oleksii Martynchuk
Doctoral Researcher HEIBRiDS

Kanishka Singh

Kanishka Singh
Doctoral Researcher HEIBRIDS

Contact

Kanishka Singh
Kanishka Singh
Project title: "Machine Learning Meets Theoretical Chemistry: Data-driven Analysis of Grapheneoxide"
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