Job ID: 98914

Master Student in Brain Data Science at the Center for Brain Research

Position: Internship

Deadline: 22 December 2022

Employment Start Date: 1 February 2023

Contract Length: 6 months

City: Vienna

Country: Austria

Institution: Medizinische Universität Wien

Department: Center for Brain Research/Neuronal Cell Biology


The Haubensak Laboratory is seeking an enthusiastic Master’s student interested in novel multidisciplinary approaches towards understanding on how genetic variance along specific sites in neuronal networks changes local circuit computations, which in turn translate into magnitude of behavioral phenotypes [1].


You project will use data mining and machine learning to retrace evolutionary hot spots in brain networks for emotions [2]–[4]. You will acquire fundamental knowledge in state-of-the art neuroscientific workflows, linking behavioral genetics to systems neuroscience.


Your profile

Ideally, you should have an academic education in natural-, computer science or medicine and hold a BSc with a solid background in and interest in computation (data processing). Previous working knowledge in Python and R programming language would be an advantage. We are looking for someone who is fascinated by working at the forefront for computational exploration of the brain, is creative, critical, and can work independently in a supportive team. It would be great to welcome you as a highly motivated and communicative member of our team, with enthusiasm to become an active element in shaping our interactive environment and workflows in interdisciplinary neuroscience.


About the Haubensak Laboratory

In the newly restructured Department of Neuronal Cell Biology, we study how circuits of interconnected neurons endow experiences and reactions with affective value (important, good or bad) – or emotions, in more general terms. Perhaps luckily so, individual brains interpret and react to the world differently. Some are more anxious, impulsive or dominant, others less. But what contributes to this diversity?

We are currently adopting integrated workflows that bridge circuit neuroscience with neurogenetic data to investigate how sets of genes and/or accumulation of genetic variance along specific sites in neuronal networks might bias circuit activity and responding, manifesting as a behavioral traits or psychiatric disease (e.g. stress disorders). Indeed, genetic variance for a given psychiatric trait maps to specific subnetworks in the brain. In the long run, this will help to understand what drives behavioral diversity in health and psychiatric conditions [4]and in the evolutionary history of the brain [5] .


More information can be found at


Expected starting date: as of February/March 2023.

The Medical University of Vienna aims to increase the proportion of women, especially in management positions and among scientific staff, and expressly encourages qualified women to apply. As one of Europe’s leading academic centers, we offer specific career programs for academic research and teaching.

Please send your application including a letter of motivation, CV, and potential references until 22/12/2012 to:

[1]           D. Pfaff, I. Tabansky, and W. Haubensak, ‘Tinbergen’s challenge for the neuroscience of behavior’, Proceedings of the National Academy of Sciences, vol. 0, p. 201903589, 2019, doi: 10.1073/pnas.1903589116.

[2]           J. Kaczanowska, F. Ganglberger, B. Galik, A. Hess, and Y. Moodley, ‘Molecular archaeology of the human brain’, bioRxiv, pp. 1–35, 2019.

[3]           F. Ganglberger, N. Swoboda, L. Frauenstein, J. Kaczanowska, W. Haubensak, and K. Bühler, ‘BrainTrawler: A visual analytics framework for iterative exploration of heterogeneous big brain data’, Computers and Graphics (Pergamon), vol. 82, pp. 304–320, Aug. 2019, doi: 10.1016/j.cag.2019.05.032.

[4]           F. Ganglberger, J. Kaczanowska, J. M. Penninger, A. Hess, K. Bühler, and W. Haubensak, ‘Predicting functional neuroanatomical maps from fusing brain networks with genetic information’, Neuroimage, vol. 170, pp. 113–120, 2018.

[5]           J. Kaczanowska et al., ‘Molecular archaeology of human cognitive traits’, Cell Rep, vol. 40, no. 9, Aug. 2022, doi: 10.1016/j.celrep.2022.111287.