ESO Summer Research Programme 2026 – Project F

Datum: 31 januari, 2026 Tid: 11:59

Placering: ESO

Mer information

Within the Directorate for Science at its Headquarters in Garching, near Munich, Germany, ESO is inviting university students to apply to our ESO Summer Research Programme. The ESO Summer Research Programme is an opportunity for university students from science, technology, engineering, and mathematics (STEM) fields who have not yet started a PhD programme and have completed at least two years of their degree.

Applications for the ESO Summer Research Programme will be considered from students taking any astronomy, physical science, computer science or mathematical degree subjects. However, it is expected that students have some knowledge of physics, programming, data analysis techniques and, preferably, astronomy.

Students will be selected for the programme based on their academic achievements, research potential and likelihood to significantly benefit from the experience. Particular attention will be given to the motivation of the students to join the programme and specific motivation for Project F:

Project F: Centers of Dwarfs or the Outskirts of Giants: Where Do More Pristine Stars Form?

The chemical composition of material within galaxies evolves as elements produced inside stars are gradually released to the surrounding medium. New generations of stars then form from this enriched material, which affects their properties and their ability to produce a wide range of energetic cosmic events - from luminous explosions seen across the electromagnetic spectrum to the formation of black holes detected through gravitational waves.

However, the pace of chemical enrichment varies among galaxies of different types. In this project, we will explore where the most chemically simple stars and related phenomena - such as superluminous and failed supernovae, and massive stellar black holes - are most likely to be found: in the many small galaxies, or in the outer regions of large galaxies. To do this, we will use a combination of semi-analytical models and Python-based computing.