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PhD in Porous Materials for Challenging Separations (Computational)

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

Project Description

A PhD studentship is available in the research group of Prof. A. I. Cooper FRS, starting in October 2020. This position will be based in the Materials Innovation Factory (MIF), a £82 M research facility that opened in 2017. The student will work in a diverse, multidisciplinary team, alongside synthetic and computational chemists, and will interact with other PhD students and postdoctoral researchers in the group.
The separation of chemical mixtures is one of the most important processes in the chemical industry. For example, feedstocks of pure ethene and propene are required for the synthesis of polyethylene and polypropylene, respectively, with more than 200 million tonnes produced annually. At present, the most common separation method is cryogenic distillation, which requires large amounts of energy for the cooling and compression of the gases. As a result, olefin purification accounts for 0.3% of the world’s total energy use.
This project will focus on developing computational methods for discovering and designing new organic solids for use in chemical separations, building in part on our recent report on ‘energy–structure–function maps’ (Nature, 2017, 543, 657). The core aim will be to understand and to predict the factors that affect adsorption selectivity, capacity and kinetics in materials and, hence, how to design better materials. This project will focus on developing computational methods and workflows to search for organic motifs for enhanced separation selectivity and to enable intelligent evolution of materials with desired properties. Both computational chemistry methods (molecular modelling & quantum mechanics) and machine learning techniques will be key technical aspects of the research.

Qualifications: Applications are welcomed from students with a 2:1 or higher Masters degree or equivalent in Chemistry, Physics, Chemical Engineering, Materials Science, or Computer Science, particularly from those with strong programming abilities and an interest in the application of machine-learning techniques to complex chemical problems.

Informal enquiries should be addressed to Prof. Cooper, .

To apply for this opportunity, please visit: and click the blue ’Apply online’ button

Funding Notes

The award is available to UK/EU residents and will pay full tuition fees and a maintenance grant for 3.5 years (£15,009 pa in 2019/2020).

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