PhD Position Context Recognition in Opportunistic Sensor Networks
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PhD Position Context Recognition in Opportunistic Sensor Networks:We offer a PhD position within the framework of the new European Research Project OPPORTUNITY.
OPPORTUNITY groups 4 high-profile European universities and research institutes. They will collaborate over the next 3 years (2009-2011) to develop systems capable of activity recognition from sensors opportunistically discovered on the user and in his neighborhood. This includes hardware, software and algorithmic innovations that will be combined in a number of technology demonstrators.
In this position you will be responsible for one of the project’s work package. You will closely collaborate with the project’s partners throughout the duration of the project.
Your work environment will be multinational, both in Zürich and with project partners within Europe, with frequent travels to the partner’s location.
Within this project, your research topics will include (but are not limited to):
- Context recognition in sensor networks.
- Unsupervised dynamic adaptation. Methods will be investigated and developed to achieve a dynamically-defined multi-parametric performance goal (e.g. accuracy, energy use) given an opportunistically discovered set of sensors and confidence and signal quality metrics. The methods will focus on the dynamic adaptation of the activity recognition chain (e.g. through dynamic resource selection, feature, and classifier adaptation) on the basis of a model of the system performance.
- Autonomous evolution in open-ended environments. New sensors, that were not available when the system was trained, can be discovered at run-time. Methods will be developed to take advantage of additional resources without the need for user intervention or system re-training. First approaches will focus on self-supervised learning – where a part of the system supervises the training of the new part of the system. Our end objective is to achieve autonomously evolving context-aware systems capable to “grow” and use new resources as they are discovered, in open-ended environment.
- Inclusion of minimal user feedback. Methods will be devised so that minimally intrusive user feedback can be used by the system to enhance activity recognition performance (e.g. sporadic annotation of an activity by the user). Constraints include maximizing information gain and minimizing user disturbance.
Scholarship Application and Further Information
Scholarship Tags: Context, Networks, Opportunistic, PhD, Position, Recognition, Sensor
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