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Machine Learning to Optimise Continuous Manufacturing of Solar Cells

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

The ability to manufacture high efficiency organic and Perovskite solar cells depends on controlling the structures that form within thin solution cast films. In this project we aim to use the ability to make in-situ observations of the film formation process together with machine learning to reactively adjust the conditions during manufacturing to continuously produce high quality films. The student will be trained in high speed microscopy methods that can capture the dynamics of drying films. In addition, the student will be trained in the industrially recognised software package Labview that will enable us to control the manufacturing process and implement adjustments based on Machine Learning algorithms and real time analysis of the microscopic observations. The PhD will involve collaboration with the large team of researchers at the University of Sheffield who are working to develop and deploy new materials for Solar Cells, and experts in Machine Learning.

Funding Notes

This is an interdisciplinary project spanning Chemistry, Physics and Engineering. The suitable candidate will have a 2.1 standard degree or equivalent qualification in these disciplines or related physical science subjects.

If English is not your first language, then you must have International English Language Testing Service (IELTS) average of 6.5 or above with at least 6.0 in each component.

Related Subjects

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