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Developing an Imaging-Diffraction Method for Corrosion and Steel Degradation Studies

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  • Full or part time
    Dr A Matamoras Veloza
    Prof A Neville
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

This project provides an excellent opportunity for doctoral training on corrosion and steel degradation as part of a collaborative work between the School of Mechanical Engineering, University of Leeds and ISIS Neutron and Muon Facility, at the STFC Rutherford Appleton Laboratory near Oxford.

This studentship will develop a non-destructive, neutron imaging and diffraction technique to study the electrochemical reactions and processes that link corrosion with carbon steel degradation at micron level.  The student will test the new tool to study the degradation mechanisms as a function of steel microstructure and corrosion evolution.  Ideally, the successful candidate will have interests in the development of electrochemical cells, in conducting experimental work as well as disposition to work on data analysis and computational modelling to meet the needs of the project.

We are looking for an enthusiastic 1st class honours or 2:1 in Chemistry, Physics, Materials or Engineering graduate to join the Institute of Functional Surfaces in September 2020. The chosen candidate will have the unique opportunity to work at ISIS, one of the world-leading centres for research in physical sciences. The post is primarily based at Leeds, but it is expected a stay of minimum one year at RAL on Harwell Campus near Oxford.

Funding Notes

This studentship is funded jointly by the Science and Technology Facilities Council (STFC) and the University of Leeds. Funding covers the cost of fees and provides a maintenance matching the Research Council UK rate (£15,144.50 for 2020/21). Funding duration is 3.5 years.

How good is research at University of Leeds in General Engineering?

FTE Category A staff submitted: 44.80

Research output data provided by the Research Excellence Framework (REF)

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