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ONE Planet DTP - Quantifying absorbing and scattering properties of atmospheric aerosol using machine learning and deep learning algorithms (OP20267)

  • Full or part time
  • Application Deadline
    Friday, January 31, 2020
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Aerosol can strongly absorb and scatter light depending on their physico-chemical properties. Quantifying aerosol-light interaction is vital for evaluating the aerosol contribution to radiative forcing (warming and cooling effect) on the atmosphere that influences global warming. Aerosols age dynamically in response to a change in environment, such as relative humidity, and their composition, size, concentration, phase (solid, liquid, gel, glass) and mixing state (core-shell) evolve. Crucially this changes the way in which each aerosol droplet or particle interacts with light. Microscopic and sub-microscopic aerosol can be characterised on an individual basis using highly focussed laser light. The light scattered by aerosol, using Raman spectroscopy and elastic scattered light measurements compared to Mie theory calculations, can reveal exquisite detail about their physico-chemical properties. However, obtaining direct measurements of the absorption properties of aerosol is more challenging and important in quantifying heating effects in the atmosphere. Analysis of bright-field RGB images using machine learning and deep learning algorithms could play a crucial role in providing detailed insight into absorptive properties by retrieving information that is otherwise hidden when using standard spectroscopic analysis techniques. This PhD will provide insight into aerosol dynamics and transformation relevant to the atmosphere and provide insight into the chemical complexities of aerosol. The successful applicant will be fully-trained in optical and spectroscopic analytical techniques at Newcastle University and RGB image processing at Northumbria University. Technical and other directly relevant training will be provided to equip the student with the skills necessary for all aspects of the project.

Funding Notes

The PhD student appointed should have a background in chemistry or physics, and a strong interest in developing computation skills and working in interdisciplinary research.

This project is part of the ONE Planet DTP. Find out more here: View Website

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