Research Associate/Senior Research Associate - Category Theory and Machine Learning (Fixed Term)

Fixed-term: The funds for this post are available for 2 years in the first instance.

Applications are invited for a Research Associate/Senior Research Associate to work on the intersection of category theory and machine learning. The position will involve working with Jamie Vicary's research group in the Department of Computer Science at the University of Cambridge, UK. In this collaborative project, we will apply insights from category theory to discover new applications of machine learning to proof construction and theorem proving. Multiple positions may be available.

Role Requirements

The successful candidate must have a PhD in mathematics, computer science or a related discipline.

This research is interdisciplinary. The candidate must have strong expertise in at least one of the following areas (1) or (2), and a clear interest in expanding their knowledge in both domains.

1. Logical methods in computer science. (For example, category theory, type theory, or proof assistants.)

2. Machine learning. (For example, graph neural networks, generative networks, or neural algorithmic learning.)

The Department of Computer Science and Technology is an academic department that encompasses computer science along with many aspects of engineering, technology and mathematics. We have a world-wide reputation for academic research with consistent top research ratings. The Department has an open and collaborative culture, supporting revolutionary fundamental computer science research, strong cross-cutting collaborations internally and externally, and ideas which transform computing outside the University.

Please follow the link at: https://www.cst.cam.ac.uk to find out more about our Department.

We offer to reimburse the cost of the first visa required to undertake employment at the Department.

For informal enquiries please contact Jamie Vicary (jamie.vicary@cl.cam.ac.uk).

Appointment at Senior Research Associate (or equivalent) level, is subject to meeting the relevant criteria and Faculty Board approval is required before any job offer can be made.

To apply online for this vacancy and to view further information about the role, please provide a CV and 1-page covering letter. The covering letter should indicate how you meet the requirements of the role. Please note referees may be contacted at any point in the assessment process. Please quote reference NR46937 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.