Fully Funded DPhil Studentship on Agile Asset Monitoring in Construction Sites
Posted: 17th July 2012
The Sensor Networks Research Group is offering a fully funded D.Phil studentship in Oxford University's Department of Computer Science in collaboration with the Oxford University’s Department of Engineering Science and Laing O’Rourke. This position is associated with the project "Agile Asset Monitoring in Construction Sites", led by Dr Niki Trigoni and Dr Andrew Markham. The project addresses the key requirement for cost-efficient tracking of people, vehicles, parts, tools and other equipment in large construction sites.
This project will involve outfitting a test construction site with a variety of sensors, including active/passive RFIDs, inertial sensors, motion detectors, magnetic sensors and GPS receivers. These sensors, which have different accuracy and energy profiles, will be connected through an ad hoc wireless network, and will be dynamically activated to take into account network conditions and tracking accuracy requirements. The focus will be on developing robust tracking techniques for challenging environments, e.g. locating objects among clutter, immersed in water or even buried underground. The project will also explore how to further improve tracking accuracy by exploiting existing CCTV infrastructure.
The studentship is fully funded at home/EU level for 3.5 years – the preferred start date is January 2013. The studentship includes a stipend of at least £13,590 per year for the duration of the studentship, as well as provision for a laptop and for travel to project meetings and conferences.
Candidates must satisfy the usual requirements for studying for a doctorate at Oxford:
http://www.cs.ox.ac.uk/admissions/dphil/dphil-criteria.pdf
We will consider students with skills in signal processing, with particular interest in probabilistic state estimation, localization and inertial tracking. Strong preference will be given to candidates with a strong drive for building embedded software systems, with excellent C/C++ programming skills.
Expertise in ad hoc sensor networks and/or computer vision (e.g. object recognition) is desirable, though not essential.
Candidates must have good writing, communication, presentation, and organization skills.
Applications can be made online here: https://apply.embark.com/grad/Oxford/16/ quoting studentship code LOR-AAM. Please put Kellogg College as your first choice college.
The closing date for applications is 31st October 2012. If you have any questions about the studentship or application process please email Julie.sheppard@cs.ox.ac.uk