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Electrical & Electronic (lighting) PhD Projects, Programs & Scholarships

We have 6 Electrical & Electronic (lighting) PhD Projects, Programs & Scholarships

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  PhD Studentship Opportunity in Computational lighting in video
  Dr S Hadfield
Applications accepted all year round

Funding Type

PhD Type

This project will explore how advances in Generative networks and deep-learning approaches to beautification and stylistic transfer, can be combined to artificially recreate the appearance of professional lighting.
  Human Centric Lighting-based Machine Learning Systems for Adult Healthcare in Intelligent Buildings
  Dr R Gibson, Dr R Roberto Ramirez-Iniguez, Dr S Sinanovic
Applications accepted all year round

Funding Type

PhD Type

SCEBE-19-016. The aim of this project is the development of a novel machine learning-based optical IPS system for adult healthcare in intelligent buildings.
  Adaptive self-learning vision systems
  Dr P Kinnell, Dr N Lohse, Dr A Soltoggio
Applications accepted all year round

Funding Type

PhD Type

Many computer vision systems make use of artificial neural networks to undertake complex image analysis tasks, such as locating objects in a clutter scene.
  Emerging application of gas plasma technologies
  Dr F Iza
Applications accepted all year round

Funding Type

PhD Type

Gas plasmas generate reactive environments at low temperature that can trigger chemical and bio-chemical processes that are hard to initiate by other means.
  PhD Studentship Opportunity in Generating virtual camera views using Generative Networks
  Dr S Hadfield, Dr A Gilbert
Applications accepted all year round

Funding Type

PhD Type

This project will explore how advances in Generative networks and deep-learning approaches to inpainting can be combined to create virtual camera views.
  Hybrid description methods for human faces
  Research Group: Communication Technologies
  Prof J A Robinson
Applications accepted all year round

Funding Type

PhD Type

The group has pioneered the application of conditional probability density estimation to describing human faces. Our methods allow a system to estimate the age, sex, race and facial expression of people in a real-time video sequence.
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