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School of Physical and Chemical Sciences
  1. School of Physical and Chemical Sciences
  2. Chemistry
  3. PhD/Research
  4. PhD Projects
  5. CO2-Sequestered Infrastructure Materials: Machine Learning for Rational Optimisation of Industrial Properties in Green Cements

CO2-Sequestered Infrastructure Materials: Machine Learning for Rational Optimisation of Industrial Properties in Green Cements

Research Group: Chemistry
Number of Students: 1
Length of Study in Years: 4 Years
Full-time Project: yes

Funding

This studentship is funded via a studentship from the China Scholarship Council.  CSC is offering a monthly stipend to cover living expenses and QMUL is waving fees and hosting the student. These scholarships are available only for Chinese candidates. 

Project Description

Inform & guide industrial processes with computational modelling and machine learning on cements and recovery from wastes, topinpoint type of additives, amounts and processing towards ideal combinations.  From nano-scale atomistic & molecular dynamics, through to bulk engineering (i.e with FEA (Finite Element Analyses)) and applying machine learning to generate a predictive comprehensive & scalable methodfor such task. Further, the nano-structural aspects of the waste processing willbe tackled with similar methods. For example,the mineralisation of CO2 with industrial waste brines to the crystalline vs amorphous forms of the output product Mg-carbonates being a standing problem for industry on bulk scalesand one to tackle computationally.  Standing problems lies in negligible computational modelling to guide the current methods for incorporation of additives to cements and the optimisation.

Requirements

Applications are invited from outstanding candidates with or expecting to receive a first or upper-second class master's degree in Chemistry or an area relevant to the project. A standalone master's degree, or undergraduate integrated master's degree are required for this position.

Applicants are encouraged to contact Dr Greg Chass on g.chass@qmul.ac.uk prior to their submission by sending a CV and a description of their interests and expertise relevant to the project.

Please use the link below to apply for this project:

https://www.qmul.ac.uk/postgraduate/research/subjects/chemistry.html

 

SPCS Academics: Dr Greg Chass

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