Research Fellow: Trusted and Explainable Positioning through Auditable Neural Networks

Current positioning solutions based on machine learning cannot achieve enough performance while retaining sufficient provability and explainability characteristics for their transformative use in industry and society. A new class of ultra-compact, high-performance biologically inspired neural network approaches can potentially provide the required level of performance and a sufficiently compact network structure to be interrogated and audited for validation and verification.

This is a research fellow role as part of an Australian Research Council Laureate Fellowship. Read more here.

To express interest in this role, please e-mail michael.milford@qut.edu.au with your CV.