Industry studentships at the EPSRC CDT in Autonomous Intelligent Machines and Systems, University of Oxford

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/Amazon Fellowship

Joint with Amazon and the EPSRC CDT in Autonomous Intelligent Machines & Systems (AIMS)

Note: This studentship is fully funded.

Supervisor(s): Ingmar Posner and Andrea Vedaldi

Start Date: October 2026

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: Learning Object-Centric World Models for Robot Perception and Interaction

Abstract

Robots that act effectively in the real world need to perceive, plan and anticipate, they need world models: internal representations that capture the entities, dynamics, and interactions that underpin their environment. A central challenge is how to map raw sensory input into the structured representations on which such models can operate. While recent work in object-centric learning has shown promise in extracting entities and relations from video, these approaches remain limited in realism, robustness, and utility, particularly in physically embodied robotics settings.

This PhD project will investigate how to learn object-centric world models that not only capture predictive dynamics but also parse complex real scenes into interpretable, robot-usable representations. Research questions include: (i) how to extract persistent, object-based abstractions from noisy video streams; (ii) how to couple these abstractions to generative world models that support prediction, counterfactual reasoning, and planning; (iii) incorporating physical priors and constraints to ensure robustness particularly in contact-rich manipulation settings, and (iv) how to evaluate and refine these models through embodied tasks such as manipulation, interaction with humans, and navigation in dynamic settings.

By bridging scene parsing with world modelling, this project will advance the foundations of robot perception and decision-making. The outcome will be representations that are simultaneously learned from video, structured around objects, and actionable in robotics, a step toward robust, general-purpose robot intelligence.

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 28^th January 2026.  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-AMAZON-2026 in the studentship reference box.

aims.robots.ox.ac.uk