About the Project
The aim of the PhD is to develop and apply innovative and efficient corrosion testing methods that can generate rapidly corrosion and performance data to support the machine learning activities. The PhD activity will focus on designing and implementing corrosion testing approaches exploiting electrochemistry, imaging and other methods, such as reliable, standardized, and representative data can be obtained for a variety of coating systems and correlated to long term performance information. The data obtained will then be used to train machine learning algorithms aiming at optimizing new coatings formulations. One of the key challenges to be addressed is the acceleration in the laboratory of specific processes that are responsible for failure in field applications. To approach the challenge, information from modelling activities and from high-resolution imaging, both part of the SusCoRD project, will be exploited. Once suitable methods for corrosion testing are developed, the data produced and the methodologies developed will also be exploited to enhance fundamental understanding of long-term failure processes.
Informal applications can be made to Michele Curioni ([Email Address Removed] )
A familiarity with electrochemistry, electrochemical testing and corrosion are required. Experience in programming or analogue circuit design is a bonus.
This project is being considered for DTA funding. This would provide a full fee waiver and a EPSRC standard stipend. International applicants are welcome to apply but will require access to self-funding.
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