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Apply for the Davie Postdoctoral Fellowship in Artificial Intelligence for Astronomy

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

Apply for the Davie Postdoctoral Fellowship in Artificial Intelligence for Astronomy
2025-02-05
(Press-News.org) February 5, 2025, Mountain View, CA -- The SETI Institute announced the Davie Postdoctoral Fellowship in Artificial Intelligence for Astronomy, inviting researchers to refine and expand ML-driven pipelines for exoplanet discovery. The successful candidate will join the SETI Institute researcher Dr. Vishal Gajjar and his team and collaborators at the SETI Institute and IIT Tirupati in India. This project will focus on enhancing supervised CNN architectures and integrating anomaly-detection techniques to uncover subtle or unconventional signals hidden within massive datasets. The application deadline is March 15, 2025. Information about how to apply is here.

“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 SETI Institute is grateful to John Davie, who supports the Davie Postdoctoral Fellowship. Davie reached out to the SETI Institute out of curiosity about its research. Through discussions with Gajjar, he wanted to be part of creating a meaningful impact.

“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.”

Instruments like TESS and Kepler have accelerated advances in exoplanet detection but have also generated enormous datasets. Supervised CNN-based classification pipelines have proven highly effective at identifying planetary signals while filtering out systematics and stellar variability. Now, these methods are evolving beyond conventional transit profiles toward more sophisticated anomaly-detection frameworks—such as autoencoders and clustering—capable of identifying unusual candidates, including ringed or disintegrating objects, megastructures, exocomets, and complex multi-planetary systems that deviate from standard spherical transit models.

The Davie Postdoctoral Fellow will conduct impactful research at the intersection of machine learning, astrophysical modeling, and interpretability, driving the next generation of exoplanet discoveries. This includes searching for megastructures to potentially address the age-old question: “Are we alone in the universe?”

https://aas.org/jobregister/ad/a17b25ac

About the SETI Institute
Founded in 1984, the SETI Institute is a non-profit, multi-disciplinary research and education organization whose mission is to lead humanity’s quest to understand the origins and prevalence of life and intelligence in the universe and to share that knowledge with the world. Our research encompasses the physical and biological sciences and leverages expertise in data analytics, machine learning and advanced signal detection technologies. The SETI Institute is a distinguished research partner for industry, academia and government agencies, including NASA and NSF.

Contact information
Rebecca McDonald
Director of Communications
SETI Institute
rmcdonald@seti.org

END

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Apply for the Davie Postdoctoral Fellowship in Artificial Intelligence for Astronomy

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[Press-News.org] Apply for the Davie Postdoctoral Fellowship in Artificial Intelligence for Astronomy
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