Fully Funded 4-year Doctoral Studentship. Joint with Iconic Games and the EPSRC CDT in Autonomous Intelligent Machines & Systems (AIMS)
Source: https://aims.robots.ox.ac.uk/newsblog-posts/fully-funded-4-year-doctoral-studentship-joint-with-iconic-games-and-the-epsrc-cdt-in-autonomous-intelligent-machines-systems-aims Parent: https://aims.robots.ox.ac.uk/newsblog-posts
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19 Dec 2024
Fully Funded 4-year Doctoral Studentship. Joint with Iconic Games and the EPSRC CDT in Autonomous Intelligent Machines & Systems (AIMS)
Fully Funded 4-year Doctoral Studentship
Joint with Iconic Games and the DPhil in Autonomous Intelligent Machines & Systems (AIMS)
Note: This studentship is fully funded.
Supervisor(s): Professor Jakob Foerster and Dr Borja González León
Start Date: October 2025
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.
Open-Ended Virtual Acting and Orchestration Systems for Videogames
Abstract
This project aims to investigate the development of autonomous virtual actors and directors for open-ended gameplay experiences. Key research questions include: How can large language models and generative architectures enable believable improvisational behaviour? How can reinforcement learning be leveraged to train both individual character performances and higher-level narrative orchestration? What mechanisms for continuous learning and memory formation best support coherent long-term character development? How is best to adapt an environment to guide the player? We will explore these questions through experimental implementations in videogame environments, focusing on measuring and improving the quality and sustainability of emergent narratives through agent interactions.
Relevant machine learning directions for this project include reinforcement learning, unsupervised environment design, LLM and VLLM agents, continual learning and long-term memory.
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 29th January 2025. 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-ICONIC-2025 in the studentship reference box.
aims.robots.ox.ac.uk
How to apply: Autonomous Intelligent Machines and Systems (EPSRC CDT) | University of Oxford
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