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Industry Studentships

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.

Fully Funded 4-year Doctoral Studentship

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

Note: This studentship is fully funded (home fees only).

Supervisor(s): Professor Phil Torr and Dr Puneet Dokania

Start Date: October 2023

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:

Foundation Models and Challenges Therein


It is evident that the recent advances in foundation models has substantially impacted research and industry with millions of people already using their applications. However, these applications largely remain limited to problems involving vision and language, and are also limited by the fact that these models involve billions of parameters training which requires gigantic amounts of data and compute that is only available to a very few worldwide. Keeping this is mind, one of the goals of this project would be to develop methods to transfer and modify the useful characteristics (e.g., robustness, semantic awareness, etc.) of the embeddings learned by these foundation models to new modalities (say, vision, lidar, and action) with limited data. The ability to do so would make it easier to adapt to new modalities without requiring internet scale data. Along with this, we would also like to investigate ways to prune and sparsify these models to be able to deploy them to low-cost devices. Since the literature related to foundation models itself is changing very rapidly and is relatively new, we will keep the proposal somewhat open-ended in order to be able to adapt to advances in this area that we might see in the near future.

Award Value

The studentship covers the full course fees (Home only) plus a stipend (tax-free maintenance grant).


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 Friday 22nd September 2023.  Candidates are therefore recommended to apply as soon as possible to and to inform when they have done so.

If you have any technical questions about the DPhil Studentship, please email

Please quote AIMS-FIVEAI-2023 in the studentship reference box.

How to apply