Postdoctoral research position in ML-supported observational astrophysics/planet formation

Applications are invited for a postdoctoral research position at the Institute for Astrophysics of the University of Cologne, Germany. Our institute is and has been strongly involved in international projects for astrophysics (VLT/I, LBT, ELT, JWST, ALMA, SOFIA, CCAT, Herschel). In the context of our recently approved Cluster of Excellence DYNAVERSE, we advertise this position to work in the area of protoplanetary disks and planet formation. We dispose to date of large and rich observational datasets on planet-forming disks obtained with numerous ground- and space-based facilities over the last two decades. These datasets cover different spatial, temporal and spectral scales/ranges and the amount of exploitable information will continue to grow steadily with the arrival of new facilities (e.g., ELT, Rubin Observatory, SKA…). We are seeking to recruit a postdoctoral researcher active in the aforementioned field who will work on combining multi-wavelength and multi-techniques observational datasets – with an emphasis on VLTI and VLT – to explore and characterize possible mechanisms of variability in young stellar objects resulting from star/disk and disk/planet interactions. In particular, we aim at exploiting grids of radiative transfer models and multi-scale hydrodynamical simulations of young disks, developed in part in the cluster, to foster the interpretation of heterogeneous and sparse snapshots by taking advantage of (un)supervised machine-learning algorithms in the spatial and spectral domain, in particular neural networks. The candidate will cooperate with computer science and ML experts from within the cluster and collaborate with our institutional partners such as (but not limited to) IPAG in Grenoble (France), UNAM in Mexico, and the MPIA in Heidelberg.

Candidates with experience in:

• Long-baseline (infrared, sub-millimeter) interferometry data reduction and interpretation
• High-resolution imaging data reduction and interpretation
• Radiative transfer/hydrodynamical models
• Python programming and first experience with ML/AI methods
• Statistical analysis of data
• Good communication skills in oral and written English

will be strongly considered for this position.

The position starts on 01.10.2026 and is funded until 30.06.2029.

The initial contract runs for two years followed by a possible extension.