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ML-tools for cloud formation in extraplanetary atmospheres	Closing date: 2025-10-10 Contact: Ruth-Sophie Taubner
This PhD position is part of a FWF funded project that aims to understand cloud formation in exoplanets and specifically the formation of molecular cluster as pre-coursers of cloud formation in the diversity of extrasolar planets. The project explores advanced neural network architectures, particularly Graph Neural Networks (GNNs) and generative models, to predict thermo-chemical properties of large molecular clusters. These data will be applied to support physical data interpretation of observations with CHEOPS, JWST, and other space missions, like PLATO, in the future.
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We seek excellent candidates with a strong background in natural sciences. Successful candidates must hold a Master's degree in physics, astrophysics or equivalent at the latest by the starting date of the position but preferably at the time of application. Previous experience on aspects of computational chemistry, machine learning and related fields, and a track record of team work will be beneficial for the selection, as will experience in scientific coding and scientific publishing. PhD Position Offer 2047 submitted on Wed Sep 03 15:40:41 CEST 2025

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