Machine Learning Engineer – (KTP Associate position)

In collaboration with Corelain, The University of Salford has been awarded a 36-month Knowledge Transfer Partnership (KTP). This position is essential to develop a digital surveyor tool (DST) focused on streamlining building defect identification, remediation and retrofit design solutions.

The DST will be scalable, user friendly and will significantly improve the capacity and capabilities of building asset managers in turn improving performance and reducing waste throughout a building lifecycle.

The position will within the building surveying team based in Corelain’s business premises in central Manchester; however, the associate will be required to travel to the University of Salford as required for meetings on a regular basis.

This KTP will develop an innovative Digital Surveyor Tool (DST) that will boost the capacity and capabilities of building managers and disrepair/retrofit surveyors, improving performance and enabling the management of larger portfolios.

The project will help to address strategic objectives by embedding an AI solution that leverages machine learning and computer vision to identify building issues. The overall aim of the project will be to evolve into a broad digital surveying tool capable of detecting various defects in buildings.