Deadline: 31st Jan. 2013
Applications are invited for a PhD Studentship starting in September 2013 within the Risk Information
Management group. The focus of this doctoral research project is on lifelong machine learning. Traditional machine learning methods learn each new problem from scratch, requiring extensive training each time. In contrast, humans rapidly learn to solve new and complex problems with limited practice by building on a lifetime of experience with related tasks and domains. The goal of this project is to develop models for lifelong machine learning, enabling experience from each encountered task and domain to be accumulated and exploited in the next. A variety of applications can be considered as lifelong machine learning has potential to impact diverse areas including computer vision, security, forensics, medical diagnosis, big data, ecommerce and others. A strong foundation in mathematics (linear algebra, calculus and statistics) and programming are essential.
The studentship will be based in the School of Electronic Engineering and Computer Science (EECS)
www.eecs.qmul.ac.uk at Queen Mary University of London, in the Risk and Information Management Group which has a world-leading reputation in the area of risk assessment. The RIM group undertakes interdisciplinary research in decision analysis and risk, databases/information retrieval, personalisation, learning, uncertainty, and Bayesian methods. Much of the research involves combining data and human expertise to create intelligent solutions for high stakes decisions. We work with practitioners to produce intelligent ‘unified models’ (typically causal Bayesian networks) that use both data and expertise as inputs, to support expert decision making in multiple application domains. The group is currently working on improved decision making in medical, legal, systems engineering, security and safety applications.
This position, funded by a Queen Mary Prinicipal’s studentship, is for 3 years and will cover student fees and a tax-free stipend starting at £15,590 per annum. Applicants of all nationalities are invited to apply. Candidates should have a first class honours degree or equivalent, or a strong Masters Degree, in computer science, mathematics, physics or electronic engineering. For queries please contact Dr. Timothy Hospedales
tmh@eecs.qmul.ac.uk.
To apply please follow the on-line process (see www.qmul.ac.uk/postgraduate/apply ) by selecting
“Computer Science” in the “A-Z list of research opportunities” and following the instructions on the right
hand side of the web page.
Please note that instead of the 'Research Proposal' we request a 'Statement of Research Interests'. Your
Statement of Research Interest should answer two questions: (i) Why are you interested in the proposed area?
(ii) What is your experience in the proposed area? Your statement should be brief: no more than 500 words or
one side of A4 paper. Mark clearly the statement with your name and the student ship title “Machine
Intelligence”. In addition we would also like you to send a sample of your written work. This might be a
chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php
Applications are invited for a PhD Studentship starting in September 2013 within the Risk Information
Management group. The focus of this doctoral research project is on lifelong machine learning. Traditional machine learning methods learn each new problem from scratch, requiring extensive training each time. In contrast, humans rapidly learn to solve new and complex problems with limited practice by building on a lifetime of experience with related tasks and domains. The goal of this project is to develop models for lifelong machine learning, enabling experience from each encountered task and domain to be accumulated and exploited in the next. A variety of applications can be considered as lifelong machine learning has potential to impact diverse areas including computer vision, security, forensics, medical diagnosis, big data, ecommerce and others. A strong foundation in mathematics (linear algebra, calculus and statistics) and programming are essential.
The studentship will be based in the School of Electronic Engineering and Computer Science (EECS)
www.eecs.qmul.ac.uk at Queen Mary University of London, in the Risk and Information Management Group which has a world-leading reputation in the area of risk assessment. The RIM group undertakes interdisciplinary research in decision analysis and risk, databases/information retrieval, personalisation, learning, uncertainty, and Bayesian methods. Much of the research involves combining data and human expertise to create intelligent solutions for high stakes decisions. We work with practitioners to produce intelligent ‘unified models’ (typically causal Bayesian networks) that use both data and expertise as inputs, to support expert decision making in multiple application domains. The group is currently working on improved decision making in medical, legal, systems engineering, security and safety applications.
This position, funded by a Queen Mary Prinicipal’s studentship, is for 3 years and will cover student fees and a tax-free stipend starting at £15,590 per annum. Applicants of all nationalities are invited to apply. Candidates should have a first class honours degree or equivalent, or a strong Masters Degree, in computer science, mathematics, physics or electronic engineering. For queries please contact Dr. Timothy Hospedales
tmh@eecs.qmul.ac.uk.
To apply please follow the on-line process (see www.qmul.ac.uk/postgraduate/apply ) by selecting
“Computer Science” in the “A-Z list of research opportunities” and following the instructions on the right
hand side of the web page.
Please note that instead of the 'Research Proposal' we request a 'Statement of Research Interests'. Your
Statement of Research Interest should answer two questions: (i) Why are you interested in the proposed area?
(ii) What is your experience in the proposed area? Your statement should be brief: no more than 500 words or
one side of A4 paper. Mark clearly the statement with your name and the student ship title “Machine
Intelligence”. In addition we would also like you to send a sample of your written work. This might be a
chapter of your final year dissertation, or a published conference or journal paper. More details can be found at: www.eecs.qmul.ac.uk/phd/apply.php
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