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Apply for the Davie Postdoctoral Fellowship in Artificial Intelligence for Astronomy
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Apply for the Davie Postdoctoral Fellowship in Artificial Intelligence for Astronomy
by Clarence Oxford
Los Angeles CA (SPX) Feb 06, 2025

The SETI Institute has introduced the Davie Postdoctoral Fellowship in Artificial Intelligence for Astronomy, inviting researchers to enhance machine learning pipelines for exoplanet detection.

The selected fellow will collaborate with Dr. Vishal Gajjar and his research team, along with partners at the SETI Institute and IIT Tirupati in India. This initiative aims to advance supervised convolutional neural network (CNN) architectures while integrating anomaly-detection methods to identify subtle or unconventional planetary signals within vast astronomical datasets. Applications are due by March 15, 2025. More details on the application process can be found at the provided link.

"Machine learning is transforming the way we search for exoplanets, allowing us to uncover hidden patterns in vast datasets," said Gajjar. "This fellowship will accelerate the development of advanced AI tools to detect not just conventional planets, but also exotic and unconventional transit signatures including potential technosignatures."

The fellowship is made possible by the support of John Davie, who was inspired by the SETI Institute's research. His interest in artificial intelligence and space exploration led him to engage with Gajjar and contribute to this effort.

"I am not a scientist, but I love space and am also enamored with the potential of AI," said Davie. "It dawned on me one morning that AI deployed against massive historical data sets could find hints of life. I'm proud to partner with the SETI Institute to help foster breakthroughs that could reshape our understanding of distant worlds and technologically advanced life on them."

Exoplanet detection has progressed rapidly with instruments such as TESS and Kepler, producing vast datasets that require advanced analytical techniques. Supervised CNN-based classification models have proven highly effective in distinguishing planetary signals from stellar variability and systematics.

Now, research is moving toward integrating anomaly-detection frameworks, including autoencoders and clustering methods, to identify unconventional candidates such as disintegrating objects, ringed planets, exocomets, megastructures, and complex multi-planet systems that defy traditional transit models.

The Davie Postdoctoral Fellow will play a critical role in bridging machine learning with astrophysical modeling and interpretability, leading to groundbreaking discoveries in exoplanet research. Their work could also contribute to the broader search for extraterrestrial intelligence by identifying structures or signals that suggest the presence of technologically advanced civilizations.

+ Davie Postdoctoral Fellowship in Artificial Intelligence for Astronomy

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