Electronic noses for the Internet of Things
A studentship opportunity in UWE Bristol's Computer Science Research Centre, in collaboration with Altered Carbon. The studentship reference is 1920-OCT-FET01.
Closing date: 18 October 2019
Start date: 1 January 2020
About the Studentship
The lower cost of wireless communications and embedded technology has given rise to the “Internet of Things,” and as of such the demand for sensing technology is more significant than any point in human history.
Industries such as medicine and agriculture are in the early stages of applying these technologies to save lives and increase efficiency. In particular, gas/vapor sensors have proven useful for everything from the detection of bacterial infection in wounds to detecting pests in crops. Traditionally, such analysis was performed using mass spectroscopy which, while accurate, is not scalable due to cost and complexity.
However, in recent times the advances in nanomaterials have allowed for the development of high-performance sensors for the detection of a vast range of analytes. It has been demonstrated that such materials can be used to produce sensors that consume microamps of power while offering sensitivity orders of magnitude higher than many traditional technologies.
Arrays of different nanomaterials can be used to form an “electronic nose” that differentiates complex gas mixtures and changes within the gaseous composition of an environment. Machine learning in combination with sensor arrays can identify complex changes in a situation such as estimating the size of bacterial colonies (Balasubramanian et al., 2008) or monitoring the healing process in wounds (Byun, Persaud and Pisanelli, 2010).
The plans for this PhD are to investigate the application of ultra-low power Binarized Neural Networks (BNNs) applied to nanomaterial arrays for the detection of complex changes in gaseous mixtures. The work undertaken will help advance the application of machine learning with state-of-the-art nanomaterial-based sensors to realise an electronic nose with low cost and power consumption useful within the domain of Internet of Things.
The student will work as part of a team, together with the supervisors and an existing team of researches, including PhD students, within UWE Bristol's Computer Science Research Centre.
For more information or an informal discussion please contact Dr Benedict Gaster at firstname.lastname@example.org.
The studentship is available from 1 January 2020 for a period of three (3) years, subject to satisfactory progress. It includes a tax-exempt stipend which is currently £15,009 per annum. In addition, full-time tuition fees will be covered for up to three years at home/EU rates, along with reasonable transport costs for visiting project partners.
International applications are accepted but the studentship will only cover the full time home/EU fees and the applicant would be expected to fund the remaining amount each year. Please see our fees web page for up-to-date information.
Applicants from outside the EU may apply for this studentship but will need to pay the difference between the ‘home/EU’ and the ‘Overseas’ tuition fees. As part of the application you will be required to confirm that you have applied for or secured this additional funding
This is a three-and-a-half-year full-time commitment. The project is ideal for a self-motivated and enthusiastic student with a good honours degree (2:1 or equivalent) in computer science or other relevant field, and evidence of further study at Masters level or equivalent in a relevant field. Ideally the candidate has a strong interest in machine learning and sensors.
How to apply
Please submit your application online. When prompted use the reference number 1920-OCT-FET01. You will need to upload your research proposal, degree certificates and transcripts and proof of as attachments to your application, so please have these available when you complete the application form.
The closing date for applications is Friday 18 October 2019.
If you have not heard from us by 31 October 2019, we thank you for your application but on this occasion you have not been successful.