Applications are invited for a full PhD Scholarship starting in April 2020 (or as soon as possible thereafter) to undertake research in the areas defined in the EPSRC project “Abstract Forward Models for Modern Games”
This project aims to provide the games industry with access to the latest and most proficient Game AI methods. Statistical Forward Planning (SFP) techniques, such as Monte Carlo Tree Search or Rolling Horizon Evolutionary Algorithms, have recently achieved remarkable performance in games research. This project addresses the main reasons behind the small uptake of SFP methods in the games industry: the lack of fast and reliable Forward Models (FM) that can be abstracted for its use by SFP algorithms in modern video-games. The PhD will be supervised by Dr. Diego Perez-Liebana (http://diego-perez.net/
The PhD will take place at the Game AI group (https://gaigresearch.github.io/
) at EECS and the project is supported by Games and Research companies such as AI Factory, Bossa Studios, Microsoft, Mountain Property Ventures, The Creative Assembly and the Defense Science and Technology Laboratory. The Game AI group is an interdisciplinary group with strong publication record and high international impact, ranking fourth in the world in activity on this field of research (http://www.kmjn.org/game-rankings/
). The Game AI group is part of the School of Electronic Engineering and Computer Science (http://www.eecs.qmul.ac.uk
), Queen Mary University of London, UK.
All applicants should have a first-class honour degree or equivalent, or a MSc degree, in Computer Science (or a related discipline). Applicants should have a good knowledge of English and an ability to express themselves clearly in both written and spoken form. The successful candidate must be strongly motivated to undertake doctoral studies, must have demonstrated the ability to work independently and to perform critical analysis.
Applicants are expected to possess fundamental knowledge and skills in two or more of the following aspects:
• Excellent knowledge of Statistical Forward Planning techniques, such as Monte Carlo Tree Search and/or Rolling Horizon Evolutionary Algorithms.
• Excellent programming skills, ideally in C++.
• Prior experience in research writing and experimental processes in the field of Game AI.
• Experience in Game Development using Game Engines.
• Experience in participation or organisation of Game AI research competitions.
All nationalities are eligible to apply for this studentship. We offer a 3-year fully funded PhD studentship supported by Queen Mary University of London including student fees and a tax-free stipend starting at £17,009 per annum. In addition to the studentship, we also welcome applications from self-funded students with relevant backgrounds.
To apply, please follow the online instructions specified by the college website for research degrees: http://www.eecs.qmul.ac.uk/phd/how-to-apply/
. Steps 2 onwards are applicable in this case. Please note that we request a ‘Statement of Research Interests’. Your statement (no more than 500 words) should answer two questions:
(i) Why are you interested in the topic described above?
(ii) What relevant experience do you have?
In addition to this, we would also like you to submit a sample of your written work. This might be a chapter of your final year or masters dissertation, or a published conference or journal paper.
In order to submit your online application you will need to visit the following webpage: https://www.qmul.ac.uk/postgraduate/research/subjects/computer-science.html
. Please scroll down the page and click on “PhD Full-time Computer Science - Semester 2 (January Start)”. The successful PhD candidate will be a member of the Game AI research group. You should mention this in your application.
Applicants interested in the post, seeking further information or feedback on their suitability are encouraged to contact Dr. Diego Perez-Liebana at [email protected]
with subject “Abstract Forward Models PhD Studentship”. All applications must be made via the website mentioned above.
The closing date for applications is 31st January, 2020.
Interviews are expected to take place in February 2020.
Starting date: April 2020.