Publisher
source

Universität Wien

PhD Positions in Cosmological Simulations – Data Science in Astrophysics & Cosmology University of Vienna in Austria

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Nov 1, 2026

Country flag

Country

Austria

University

Universität Wien

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Keywords

Computer Science
Data Science
Machine Learning
Mathematics
Astronomy
Astrophysics
Cosmology
Computational Science
Python Programming
General Relativity
Galaxy Formation
Statistics
Physics

About this position

The University of Vienna invites applications for three PhD positions in Cosmological Simulations within the Department of Astrophysics, part of the Faculty of Earth Sciences, Geography and Astronomy. The research group 'Data Science in Astrophysics & Cosmology' focuses on cutting-edge research in data and computational science applied to astrophysics and cosmology. Successful candidates will join a vibrant, international team and participate in the new DUNE (Dark UNiverse Explorations) research network, collaborating with the Universities of Vienna and Innsbruck and IST Austria.

Two PhD students will work on cosmological inference using simulations under the supervision of Dr. Oliver Hahn, while one PhD student will analyze cosmological hydrodynamic simulations with new statistical methods under Dr. Sylvia Ploeckinger. The group values a positive, inclusive, and respectful working environment, with regular meetings and interdisciplinary collaboration. Members benefit from privileged access to the Vienna Scientific Cluster (VSC) supercomputing facility and Austria's membership in ESO and ESA, providing access to world-class observing facilities.

Applicants should hold a completed Diploma or Master's degree in Physics, Astrophysics, or related fields (e.g., Computational Science, Applied Mathematics). Required skills include excellent English proficiency (C1), strong Python programming abilities, experience in academic writing, and effective teamwork and communication. Additional expertise in high-performance computing, statistics, machine learning, or astrophysics (especially cosmology, galaxy formation, or general relativity) is advantageous.

The positions offer a basic gross salary of EUR 3,714.80 per month (on a 40h/week basis), paid 14 times a year, with employment initially limited to 1.5 years and automatic extension up to February 28, 2030 unless terminated within the first 12 months. The start date is negotiable, and the workplace is located in the scenic Observatory Park in Vienna, with excellent public transport and flexible working hours. The University of Vienna is committed to equal opportunities, diversity, and the advancement of women, with preference given to female candidates in case of equal qualifications.

To apply, submit a motivation letter (max. one page), CV (including research activities, achievements, interests, and publications), PDF or cloud link to your Master's thesis, one letter of reference sent directly to [email protected] referencing job ad ID 5059, and bachelor's/master's diploma and transcripts via the job portal 'Apply now' button. Please indicate your project preference in the motivation letter if applicable. The application deadline is November 1, 2026.

For more information about the research group and projects, visit https://cosmology.univie.ac.at. For questions, contact Dr. Oliver Hahn at [email protected].

Funding details

Available

What's required

Applicants must hold a completed Diploma or Master's degree in Physics, Astrophysics, or related fields such as Computational Science or Applied Mathematics. Excellent command of written and spoken English at C1 level is required. Candidates must demonstrate excellent programming skills in Python and its scientific applications, experience in academic writing, strong teamwork and communication abilities, and high motivation for scientific excellence. Additional knowledge in high-performance computing, statistics, machine learning, or (astro-)physics (especially cosmology, galaxy formation, or general relativity) is advantageous.

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