re: A fully funded Ph.D. graduate study in Uppsala University, Sweden, Department of Information Technology, Division of Systems and Control (4yrs.+1yr. teaching).
subject: This position aims at relating modern insights in (Online) Machine Learning (OML) to the esteemed tool of the Kalman Filter (KF). OML has led to renewed insights in such tasks as optimisation, multiple-armed bandits algorithms and sequential designs, and comes nowadays with a solid theoretical underpinning. The KF on the other hand, is the workhorse of the control-engineering, and is for example used as the observer of choice in many state-feedback schemes. It is analysed and found optimal in different settings.
Integrating KFs in a modern OML setting would provide new insights and modifications to basic schemes.The particular focus would be how to extend the KF to the case of many variables. Modern techniques of OML have excelled in this topic, while traditional approaches as the KF have not been studied extensively in this regard.
practical: This is a full-time funded position covering five years in total. The successful candidate is expected to spend 80% on this research challenge, as well as 20% on assisting in different (under-) graduate courses taught at our division. The deadline for application is April 22 (right after easter). The applicant can start directly after that (in spring 2014). Supervisor for this project is Kristiaan Pelckmans. Do send an email to kp@it.uu.se for further enquiries. Swedish citizens are encouraged to apply. A link to the official site of application will be sent out via the same channels in the near future.
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