Post Doctoral Fellow

• Loomba University of New Mexico
• 4.1 Albuquerque, NM Job Details 15 hours ago Qualifications AI models Image processing Computer vision AI tools proficiency Machine intelligence Doctor of Philosophy Machine learning (ML) fundamentals Full Job Description Post Doctoral Fellow
• Loomba Posting Number req37782 Employment Type Faculty Faculty Type Research Hiring Department Physics Astronomy Department (707A) Academic Location College of Arts & Sciences Campus Main
• Albuquerque, NM Benefits Eligible Postdoctoral Fellows may be eligible to receive certain UNM benefits .

See the Benefits home page for more information. Position Summary 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.

Qualifications Minimum Qualifications:

PhD in Physics, Astronomy or a related field by date of appointment The applicants must demonstrate strong expertise in artificial intelligence, machine learning, and image processing

Preferred Qualifications:

Prior postdoctoral or equivalent research experience is preferred as evidenced by the CV Experience applying AI techniques for rare searches, such as for dark matter, where high efficiency signal detection and background rejection are required as evidenced by the CV Experience working across a broad domain of data types: from 1D waveforms (e.g., time series) and 2D images (e.g., taken with CMOS cameras), to high dimension images such as those used in radio astronomy as evidenced by the CV Experience working in a team that includes senior and junior researchers and graduate students as evidenced by the CV Excellent interpersonal, written and verbal communication skills as evidenced by the CV A demonstrated commitment to cultivate an understanding of the rich and varied cultures of New Mexico and to the success of the University's mission to serve local and global communities Application Instructions Only applications submitted through the official UNMJobs site will be accepted. If you are viewing this job advertisement on a 3rd party site, please visit UNMJobs to submit an application. Please upload a current CV to your application. Applicants who are appointed to a UNM faculty position are required to provide an official certification of successful completion of all degree requirements prior to their initial employment with UNM. For Best Consideration For best consideration, please apply by . This position will remain open until filled. The University of New Mexico is committed to hiring and retaining a diverse workforce. We are an Equal Opportunity Employer, making decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, veteran status, disability, or any other protected class.