Industry studentships at the EPSRC CDT in Autonomous Intelligent Machines and Systems, University of Oxford
Industry studentships
The CDT offers a number of industry funded studentships. These are either partially or fully funded, and will be advertised here.
When applying to one of these studentships, please quote the reference number in the application form, and inform the CDT Administrator that you have applied for this studentship when submitting your application.
Details on how to apply can be found here: https://www.ox.ac.uk/admissions/graduate/courses/autonomous-intelligent-machines-and-systems
Fully Funded 4-year Doctoral Studentship
Joint with MathWorks and the EPSRC CDT in Autonomous Intelligent Machines & Systems (AIMS)
Note: This studentship is fully funded.
Supervisor(s): Yarin Gal
Start Date: October 2027
Autonomous systems powered by artificial intelligence will have a transformative impact on economy, industry and society as a whole. Our mission is to train cohorts with both theoretical, practical and systems skills in autonomous systems - comprising machine learning, robotics, sensor systems and verification- and a deep understanding of the cross-disciplinary requirements of these domains. Industrial partnerships have been and will continue to be at the heart of AIMS, shaping its training and ensuring the delivery of Oxford’s world-leading research in autonomous systems to a wide variety of sectors, including smart health, transport, finance, energy and extreme environments. Given the broad importance of autonomous systems, AIMS provides training on the ethical, governance, economic and societal implications of autonomous systems. For more information regarding the AIMS programme, see our web pages at: aims.robots.ox.ac.uk.
Title: Uncertainty in LLM-based code generation
Abstract
This foundational research will focus on defining and measuring uncertainty in code generation. We will develop methods based on semantic probes to tradeoff speed versus accuracy, and based on the discovered uncertainty methods, we will explore the use of curriculum learning and active learning to reduce data costs. The resulting output will be a set of methods for verifying the confidence of generated code, assessing correlations both with intended utility as well as ability to successfully compile.
Award Value
The studentship covers the full course fees plus a stipend (tax-free maintenance grant).
Eligibility
Prospective candidates will be judged according to how well they meet the following criteria:
Applicants are normally expected to be predicted or have achieved a first-class or strong upper second-class undergraduate degree with honours (or equivalent international qualifications), as a minimum, in computer science, engineering, physics, mathematics, statistics or other related disciplines. A previous master's qualification is not required.
Excellent English written and spoken communication skills
Candidates will also need to demonstrate a broad interest in some or all of the four AIMS themes:
machine learning, as a unifying core
robotics & vision
cyber-physical systems (e.g. sensor networks)
control & verification
The deadline for applying is Wednesday 27th January 2027. Candidates are therefore recommended to apply as soon as possible to and to inform wendy.adams@eng.ox.ac.uk when they have done so.
If you have any technical questions about the DPhil Studentship, please email wendy.adams@eng.ox.ac.uk
Please quote AIMS-MATHWORKS-2027 in the studentship reference box.