Job description

FASTBLADE needs a researcher with advanced data skills that can translate and convert environmental conditions into load cases for the application to structural testing protocols. The researcher will also pilot machine learning techniques to extend the operating range of the FASTBLADE system, a first of its kind regenerative hydraulic system, designed specifically for testing tidal turbine blades.

The researcher will work at one of the premier global fatigue test facilities, and they will contribute to test standards and processes that will become global benchmarks of structural testing excellence.

The aim of this postdoctoral fellowship is to accelerate the design and development of large composite structures through the application of data-driven techniques that extend the capability and service offerings of the FASTBLADE facility.

The fellow’s work will ensure that FASTBLADE reaches its full potential by improving the operational inputs (system settings and characterised in-service load data) and outputs (machine response and specimen response) using data-driven techniques.

How to apply

If you have any questions please contact

Dr Jeff Steynor, Project Manager of FASTBLADE, jeff.steynor@ed.ac.uk
and Dr Encarni Medina-Lopez, Chancellor’s Fellow in Data Driven Innovation, emedina@ed.ac.uk