Academic Researcher (Astrophysics/Machine Learning) | Akademische*r Mitarbeiter*in (w/m/d)

Job Summary

This full-time, fixed-term position at the Institute for Theoretical Astrophysics, Heidelberg University, is for an Academic Researcher to lead a project focused on developing an invertible neural network to predict stellar properties from photometric and spectroscopic observations. The role involves compiling training data from stellar evolution simulations, generating synthetic observations, and applying the developed tools to observational data from star-forming regions to reconstruct detailed star formation histories. The successful candidate will also be responsible for documenting software, publishing research findings in peer-reviewed journals and conferences, contributing to workshops, and mentoring students. This is an exciting opportunity to contribute to the cutting-edge StarForML group, which specializes in robust machine learning methods for star formation observations, within one of Germany's largest astronomy hubs.