Research Associate in Machine Learning for Astronomical Imaging (Fixed Term)

CamWebDir; United Kingdom; Remote; GBP 40,000 - 55,000; Full time

Job summary

The University of Cambridge seeks two Research Associates to advance machine learning for astronomical imaging, led by Dr Miles Cranmer and Prof. Vasily Belokurov.

You will develop AI methods for detecting and characterising low surface brightness features and build scalable software pipelines for large survey data.

Qualifications

• PhD (or near completion) in ML/CS/astronomy/physics/mathematics or related field.
• Experience with ML for imaging or astronomy is desirable.
• Ability to communicate technical material clearly and collaborate across groups.

Responsibilities

• Conduct original and collaborative research in ML for astronomical imaging.
• Develop software stack for data pipelines, model training, evaluation, and deployment on GPU/HPC systems.
• Publish results in peer-reviewed venues and release open-source software.

Skills

Research

Machine learning

Scientific computing

Education

PhD (or near completion) in ML/CS/astronomy/physics/mathematics

Tools

GPU computing

HPC systems

Job description

# Research Associate in Machine Learning for Astronomical Imaging (Fixed Term)UUniversity of CambridgeCB2 1TNvia University of CambridgePosted 7 Sep 2026Apply on cam.ac.uk →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 primary function of these posts is research and innovation: developing novel AI methods for the detection and characterisation of low surface brightness structure in wide-field astronomical imaging and advancing these methods to operate at the scale of forthcoming surveys.The role holders will conduct original research into machine learning approaches to source detection, deblending, and low surface brightness feature recovery, including generative and simulation-based methods. Alongside this research, they will design and build the software stack that applies these methods to large survey datasets, spanning data pipelines, model training and evaluation infrastructure, and deployment on GPU and HPC systems. They will publish their results in peer-reviewed journals, present at international conferences, and release open-source research software associated with the programme.The posts are funded by a philanthropic gift. The role holders will work closely with the research groups of Dr. Cranmer and Professor Belokurov and will have access to new wide-field imaging data through the programme's links to observational surveys.Duties include developing and conducting individual and collaborative research objectives, proposals and projects. The role holders will be expected to plan and manage their own research and administration, with guidance if required, and to assist in the preparation of proposals and applications to external bodies. You must be able to communicate material of a technical nature and be able to build internal and external contacts. You may be asked to assist in the supervision of student projects, the development of student research skills, provide instruction, or plan/deliver seminars relating to the research area.The successful candidate will have a PhD, or close to completion of a PhD (thesis submitted), or equivalent research experience to a PhD, in machine learning, computer science, astronomy, physics, mathematics, or a related computational discipline.Fixed-term: The funds for this post are available for 3 years in the first instance.Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.Informal inquiries can be made by contacting Dr Miles Cranmer at [e-mail] you have any queries about the application process, please email [e-mail] closing date for applications is Monday, 21 September 2026.Interviews will be held shortly after the closing date.Please indicate the contact details of two academic referees on the online application form and upload a full curriculum vitae and a description of your recent research (not to exceed three pages). Please ensure that at least one of your referees is contactable at any time during the selection process and is made aware that they will be contacted by the Mathematics HR Office Administrator to request that they upload a reference for you to our Web Recruitment System; and please encourage them to do so promptly.Please quote reference LE50980 on your application and in any correspondence about this vacancy.The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.The University has a responsibility to ensure that all employees are eligible to live and work in the UK.