As a quick follow-up to yesterday’s article on quantifying technosignature data, I want to mention the SETI Institute’s invitation for applicants to the Davie Postdoctoral Fellowship in Artificial Intelligence for Astronomy. The Institute’s Vishal Gajjar and his collaborators both in the US and at IIT Tirupati in India will be working with the chosen candidate to focus on neural networks optimized for processing image data, so-called ‘CNN architectures’ that can uncover unusual signals in massive datasets.
“Machine learning is transforming the way we search for exoplanets, allowing us to uncover hidden patterns in vast datasets,” says 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.”
As AI matures, the exploration of datasets is a critical matter as these results from missions like TESS and Kepler are packed with both exoplanet data as well as stellar activity and systematics that can mislead investigators. Frameworks for sifting out anomalies should help us distinguish unusual candidates including disintegrating objects, planets with rings, exocomets and perhaps even megastructures and other technosignatures, all flagged by their deviation from our widely used transit models.
The data continue to accumulate even as our AI tools sharpen to look for anomalies. I can think of several Centauri Dreams readers who should find this work right up their alley. If you’re interested, you can find everything you need to apply for the fellowship here. The deadline for applications is March 15, 2025.
