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Civil & Structural Engineering (neural engineering) PhD Projects, Programs & Scholarships

We have 5 Civil & Structural Engineering (neural engineering) PhD Projects, Programs & Scholarships

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  Global resilience assessment using artificial neural network
  Research Group: Future Cities Research Network
  Dr M Imani
Applications accepted all year round

Funding Type

PhD Type

Research Group. Future Cities Research Network - https://www.anglia.ac.uk/science-and-engineering/research/institutes-and-groups/future-cities.
  Scalable and Energy-Efficient Multi-Building and Multi-Floor Indoor Localisation/Navigation based on Deep Neural Networks with a Multivariate Database
  Dr K Kim
Applications accepted all year round

Funding Type

PhD Type

The candidate should have a first class or upper second class honours degree, or a master’s degree (or equivalent qualification), in Evidence of good spoken and written English is essential.
  The value of automation for infrastructure decision making
  Dr M Perry, Dr J Quigley, Dr E Tubaldi
Application Deadline: 26 March 2020

Funding Type

PhD Type

This PhD project will quantify the value of automating infrastructure health monitoring.
  Computer Vision and Machine Learning for ReIdentification of Civil Structural Components and their Damage Analysis.
  Dr B Mandal
Applications accepted all year round

Funding Type

PhD Type

In this project the PhD research student needs to understand the strengths and weaknesses of the deep convolutional neural network for analysis of civil infrastructures, such as concrete bridges, highways, buildings, tunnels and stadiums, using videos/images captured by a network of multiple cameras.
  A combined framework of machine learning and full waveform inversion for electromagnetic sensing of infrastructure (Advert Reference: SF19/EE/MCE/WARREN)
  Dr C Warren, Prof J Martin
Applications accepted all year round

Funding Type

PhD Type

Electromagnetic sensing systems, such as Ground Penetrating Radar (GPR), are critical tools to allow us to evaluate the condition of surface and sub-surface infrastructure.
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