Join DERI
We collaborate with a range of partners across Queen Mary and internationally to support our mission to deliver innovation and drive impact in digital, data science, and AI. As a member of DERI, you'll be part of a world-leading network of researchers and receive access to exclusive events and opportunities.
There are multiple ways to engage with DERI which include:
Vacancies: Current vacancies to join us to work at DERI will appear below when available.
Fellowship Opportunities: We welcome expressions of interest from excellent candidates looking to apply for independent Fellowships hosted in DERI. Further details can be found on our dedicated Fellowships Page.
Visiting Academics: We seek to collaborate widely with researchers from across the world, and welcome expressions of interest from those waiting to collaborate with DERI as part of a visiting researcher opportunity. We encourage anyone in this position to research out directly to our academic staff, to discuss collaborations and opportunities in DERI. You can see further details of our staff on the Research Team pages.
PhD Opportunities: Students looking to undertake a PhD in DERI are encouraged to review our Research Teams and contact potential supervisors directly to discuss opportunities. Any funded positions available within DERI will be added to the vacancies section below.
You can also connect with DERI via our social media channels:
Linkedin: linkedin.com/in/qm-deri
X: @DERI_QMUL
Current Vacancies
Roles available in DERI will be advertised here, as well as on the Queen Mary job pages when available.
Postdoctoral Research Associate in AI Surrogate Models for Forward Stratigraphic Modelling - Deadline June 8th 2025
We are looking for a motivated Postdoctoral Research Associate (PDRA) to join the research group of Professor Cédric John. You will be a key member of the John Lab, contributing to our development and the success of our mission, as well as collaborating with key members of TOTAL OneTech, which will include regular visits to Pau (France) to present results.
The aim of the research project is to develop a series of surrogate models focusing notably on Physics-Informed Neural Networks to emulate the process of sediment deposition, diagenesis, and potentially fracturing, working closely with partners in TOTAL OneTech, and members of Professor John's research group.
Find out more about the role, and how to apply on the Queen Mary Jobs Pages.
