This FWF funded project, 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.
Your tasks
Your Tasks
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.
APPLICATION PROCESS
The first stage of the application process is anonymised, the second stage takes the form of an interview. To apply for these positions, an anonymous questionnaire (weblink here) has to be filled in. No further documents are required at this stage of the application process. The form includes questions about scientific skills, the candidate’s master thesis, information on master courses taken, asks for a statement of interest and a statement about research integrity. Please, submit the form no later than October 10th, 2025. Interviews are planned for the beginning of November 2025.
Our Offer
The appointment can begin January 01st, 2026 (an earlier or later start may be negotiable) for a duration of 3.5 years.
We offer an annual gross salary of € 39.208,79, according to the collective agreement of the OeAW.
The Austrian Academy of Sciences (OeAW) pursues a non-discriminatory employment policy and values equal opportunities, as well as diversity. Individuals from underrepresented groups are particularly encouraged to apply.
