Post Doctoral Fellow - Loomba
University of New Mexico
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- Location
- Albuquerque, United States
- Job type
- Full time
- Posted
- Oct 13
- 7 days ago
Career path
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About the company
University of New Mexico
Higher education · 2 to 10 employees
What you'll do
- Design and develop an integrated, cross-domain image-processing pipeline based on a common AI workflow.
- Apply it to rare-event searches, strong gravitational lens images, and transient identification in interferometric radio astronomy.
What you need
- Applicants must demonstrate strong expertise in artificial intelligence, machine learning, and image processing.
Key skills
artificial intelligencemachine learningimage processing
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Read the full posting here
The Department of Physics and Astronomy at the University of New Mexico (UNM) invites applications for two Postdoctoral/Research Scientist positions to contribute to a DOE-funded GENESIS research program applying artificial intelligence and machine learning across several domains of physics and astronomy. The successful candidates will develop an integrated image-processing pipeline and apply it to three distinct problems: rare-event searches in particle detectors (e.g. the Migdal effect and neutrino-less double beta decay), component separation in images of strong gravitational lenses, and transient identification in interferometric radio astronomy. The positions offer an unusual opportunity to develop methods that generalize across experimental particle physics, cosmology, and radio astronomy. The selected scientist(s) will be working with a multidisciplinary team at the University of New Mexico including Professor Dinesh Loomba (PI), Professor Francis-Yan Cyr-Racine, Professor Huaiyu Duan, Distinguished Professor Greg Taylor, and Research Associate Professor Jayce Dowell, together with Dr. Ralph Massarczyk at LANL.
The applicants must demonstrate strong expertise in artificial intelligence, machine learning, and image processing. The duties of this position will be to design and develop an integrated, cross-domain image-processing pipeline based on a common AI workflow in collaboration with researchers in experimental particle physics, cosmology, and radio astronomy.