|Funding for:||UK Students, EU Students|
|Funding amount:||£16,500 p.a. plus yearly increments|
|Placed On:||18th July 2018|
|Closes:||31st August 2018|
Accurate indoor localisation is a rich source of information for understanding human behaviour. Video depth cameras and wearable technologies can provide high levels of accuracy and represent the state-of-the-art in indoor localisation. Wearable technologies (such as wristbands) tend to have limited battery life and have a high risk of non-compliance; commercial solutions based on UWB (Time of Arrival - ToA) can offer very accurate positioning, but with power consumption, the battery life of the wearable tag is several hours (too short for long term localisation applications).
This project focusses instead on a next generation of localisation, which exploits ambient Radio Frequency (RF) signals through passive radar technology. RF signals such as those arising from Wi-Fi transmissions typically cover entire households. The reflections of these signals from people provide a rich source that can be used to determine localisation information (and in some cases physical activity information), without requiring a wearable device and with much lower perceived intrusion.
The project will utilise the latest ideas in Artificial Intelligence / Deep Learning for automatic recognition of physical activities augmented by indoor localisation capabilities. The successful PhD student will join a large team working on recently funded OPERA project.
How to apply:
Please make an online application for this project at http://www.bris.ac.uk/pg-howtoapply. Please select < Electrical & Electronic Engineering> on the Programme Choice page and enter details of the studentship when prompted in the Funding and Research Details sections of the form with the name of the supervisor.
We are looking for an enthusiastic student with either a First class / minimum 2:1 honours degree or equivalent in Computer Science, Electrical & Electronic Engineering, Physics or Maths.
Basic skills and knowledge required in Solid Mathematics (statistics, multivariate calculus, optimisation, matrix algebra); Programming (python, matlab).
The studentship is funded for 4 years by EPSRC and co-funded by our industrial partner Toshiba Research Europe Ltd. The eligibility criteria are listed on EPSRC website. (EU candidates please contact email@example.com before filing your application). Substantial allowance for conference travel and consumables is also provided.
Please contact Dr Robert Piechocki, firstname.lastname@example.org
For general enquiries, please email email@example.com
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