Undergraduate students interested in writing a Masters or PhD thesis at the Max Planck Institute for Astronomy at a later date are encouraged to apply for a "Miniforschungsprojekt", literally "miniature research project" at the institute. Such projects are designed to bring students into early contact with astrophysical research. Sometimes, project work can be continued in the context of a Masters thesis.
The offer is aimed at students in physics, mathematics, computer science and related scientific areas. The topics offered here are also suitable for a summer internship at MPIA, details of which can be found here:
Miniforschung projects typically take between 6 and 8 weeks to complete and should be undertaken when classes are not in session. Subject areas include astronomical data analysis, numerical simulations, and work connected with the design and construction of new astronomical instruments.
If you're an undergraduate with one of the above-mentioned majors, you should also feel free to apply for internships outside of the Miniforschung framework. Just ask!
The following topics are available for Miniforschung projects:
PROJECTS LISTED BELOW
Students interested in a Miniforschungs project should send their application including a CV to the contact person stated in the project.
Miniforschungs projects can also be converted in a bachelor project.
For students from Univ. Heidelberg our Mini-Forschung counts as Module "Projektpraktikum Code WPProj" and will lead to the appropriate number of Credit Points. For details see the Module-Handbuch Bachelor of Science Physik:
Infrared Space Astronomy
The IR Space Astronomy Group at MPIA offers a variety of projects in the field of astronomy and instrumentation. More information about the group can be found on http://www.mpia.de/IRSPACE/.
Numerical Experiments in Planet and Star Formation
- Turbulence in protoplanetary accretion disks
- Super computing on Massive Compute Clusters
- Grid and non-Grid Methods for Hydro Dynamics
- 3D visualization of simulated data
- Analysis of Big Data Sets