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Detection and tracking of transient phenomena from heterogenous data and data-driven methods (EPS2022/54)

   School of Engineering & Physical Sciences

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  Prof S McLaughlin, Dr Y Altmann  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

While Bayesian filters are massively used for object detection and tracking, the development of data-driven approaches has recently shown that traditional tracking algorithms can benefit from pre-trained artificial neural networks (ANNs) in terms of computational complexity and/or prediction accuracy. Such advantages are particularly appealing when information is acquired by several sensors, some of which having imaging capabilities. In this project, the student will investigate multiple extended object detection and tracking, using sensor fusion in a surveillance context. In particular, they will investigate how to combine information arising from imaging modalities such as hyperspectral images, neuromorphic images or Lidar point, combined with arrays of presence sensors, providing asynchronous observations. While the Bayesian formalism and variational inference will be adopted for uncertainty management, image-based neural networks will be investigated for feature recognition/selection and to create data-driven priors within tracking algorithms.

How to Apply

1. Important Information before you Apply

When applying through the Heriot-Watt on-line system please ensure you provide the following information:

(a) in ‘Study Option’

You will need to select ‘Edinburgh’ and ‘Postgraduate Research’. ‘Programme’ presents you with a drop-down menu. Choose Chemistry PhD, Physics PhD, Chemical Engineering PhD, Mechanical Engineering PhD, Bio-science & Bio-Engineering PhD or Electrical PhD as appropriate and select September 2022 for study option (this can be updated at a later date if required)

(b) in ‘Research Project Information’

You will be provided with a free text box for details of your research project. Enter Title and Reference number of the project for which you are applying and also enter the potential supervisor’s name.

This information will greatly assist us in tracking your application.

Please note that once you have submitted your application, it will not be considered until you have uploaded your CV and transcripts. 

Funding Notes

All applicants must have or expect to have a 1st class, MPhys, MSci, MEng or equivalent degree by Autumn 2022. Selection will be based on academic excellence and research potential, and all short-listed applicants will be interviewed (in person or online). Applications may be accepted for exceptional overseas applicants.
There are a number of scholarships available which offer funding from between 3 and 3.5 years at an average stipend rate of £15,000 per year.
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