**Job Description**
This postdoctoral position involves using advanced Machine Learning techniques to predict the anharmonic vibrational spectra of large Polycyclic Aromatic Hydrocarbon (PAH) molecules. These PAHs are prevalent in astronomical environments and are responsible for significant vibrational emission features in galaxy spectra. The project aims to develop computationally efficient methods for calculating anharmonic spectra, which are otherwise very expensive using traditional Density Functional Theory (DFT) methods for large astronomical PAHs.
**Skills & Abilities**
• Experience with using neural networks and topological descriptors for molecules or materials
• Excellent communication and writing skills in English
• Ability to work both independently and collaboratively in a multidisciplinary environment
**Qualifications**
Required Degree(s) in:
• Chemistry
• Physics
• Astronomy
• Computer Science
• A related field
(Ph.D. completed upon arrival)
**Experience**
Other:
• Demonstrated commitment as a researcher through previous research experiences, publications, and presentations at international conferences
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