Applications are invited for a full PhD Scholarship starting April 2020 (or as soon as possible thereafter) to undertake research in the area of computer vision for robot perception.
Active perception of an environment through artificial intelligence (AI) relies heavily on the design of architectures and their extensive training to generate compact representations. Taking advantages of recent advancements in deep learning, these representations have shown significant improvement in building new knowledge and acquiring new skills for AI agents.
In this project, the student will investigate a principled integration of computer vision techniques for embodied agents, which take decision based on high-dimensional sensory signals. Specifically, this project will investigate how visual signals can be processed by computer vision technique to effectively perceive a dynamic and cluttered environment. Through this project, the student will investigate to bridge the gap between state-of-the-art computer vision techniques and vision-based embodied perception.
The PhD will be supervised by Dr Changjae Oh and will be based in the Computer Vision Group (http://vision.eecs.qmul.ac.uk/) and Centre for Intelligent Sensing (http://cis.eecs.qmul.ac.uk/), groups with strong publication record and high international impact, which 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 Electronic Engineering (or a related discipline). Applicants should have a good knowledge of English and ability to express themselves clearly in both written and spoken form. The successful candidate must be strongly motivated to undertake doctoral studies, as well as must have demonstrated the ability to work independently and perform critical analysis. A record of publishing research in international conferences and/or journals would be highly desirable, as well as a strong track record of working in international teams.
The essential selection criteria include:
• Experience in Machine Learning and/or Computer vision.
• Good coding skills in Python, Matlab and/or C++.
• Ability to work independently or as part of a team.
The desirable selection criteria include:
• Experience and knowledge of deep learning techniques
• Experience in robot system.
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 3 (April Start)”. The successful PhD candidate will be a member of the Computer Vision Group and Centre for Intelligent Sensing. 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 Changjae Oh at email@example.com with subject “Computer Vision for Robot Perception PhD Studentship”. All applications must be made via the website mentioned above.
The closing date for applications is 29th February 2020.
Interviews are expected to take place in March 2020.
Starting date: April 2020.