ML Research Associate: Astronomical Imaging

University of Cambridge; Cambridge; On-site; GBP 37,000 - 46,000; Full time

Job summary

The University of Cambridge seeks two Research Associates to join an ambitious research programme in machine learning for astronomical imaging, led by Dr. Miles Cranmer (DAMTP/IoA) and Professor Vasily Belokurov (IoA).

The role focuses on developing novel AI methods for detecting and characterising low surface brightness features in wide-field imaging and scaling these methods to forthcoming surveys. You will design and build the software stack for large survey data, spanning pipelines, training

Qualifications

• PhD or near completion in machine learning, computer science, astronomy, physics, mathematics, or related computational discipline.
• Strong ability to communicate technical material to diverse audiences.
• Willingness to supervise student projects and deliver seminars.

Responsibilities

• Conduct original research on ML approaches to source detection, deblending and low surface brightness feature recovery.
• Develop and maintain software stack for large survey datasets, including data pipelines, training/inference infrastructure, and HPC deployment.
• Publish results in peer-reviewed journals and present at international conferences; release open-source software.

Education

PhD or close to completion (thesis submitted)

Job description

The University of Cambridge seeks two Research Associates to join an ambitious research programme in machine learning for astronomical imaging, led by Dr. Miles Cranmer (DAMTP/IoA) and Professor Vasily Belokurov (IoA).

The role focuses on developing novel AI methods for detecting and characterising low surface brightness features in wide-field imaging and scaling these methods to forthcoming surveys. You will design and build the software stack for large survey data, spanning pipelines, training