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

Data Science Jobs UK; Cambridge; On-site; GBP 37,000 - 46,000; Full time

Job summary

Data Science Jobs UK advertises a Research Associate position at the University of Cambridge. The role focuses on machine learning for astronomical imaging, including developing novel AI methods for detecting low surface brightness features in wide-field data.

The successful candidate should have a PhD (or near completion) in a related field and will contribute to data pipelines, model training, and deployment on GPU/HPC systems, publishing results and releasing open-source tools.

Qualifications

• PhD or near completion in ML, CS, astronomy, physics, mathematics or related field.
• Experience conducting independent and collaborative research.

Responsibilities

• Develop and conduct individual and collaborative research objectives and projects.
• Design and build the software stack for large survey datasets, including pipelines and training infrastructure.
• Publish results in peer-reviewed journals and present at international conferences.
• Release open-source research software associated with the programme.
• Collaborate with the Cranmer and Belokurov groups and access wide-field imaging data.

Skills

Machine learning

Research

Scientific programming

Data pipelines

Education

PhD or near completion

Tools

GPU

HPC

Open-source software

Job description

Salary

£37 – £46 pa

Job Type

Contract

Work Pattern

Full-time

Closing Date

The closing date for applications is Monday, 21 September 2026.

Fixed-term

The funds for this post are available for 3 years in the first instance.

The University of Cambridge seeks a Research Associate 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 holder 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 post is funded by a philanthropic gift. The role holder 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 holder 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 PhD, in machine learning, computer science, astronomy, physics, mathematics, or a related computational discipline.

Informal inquiries can be made by contacting Dr Miles Cranmer at .

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.