Internship opportunity available at PARC in System Failure Modeling, Monitoring, Prognostics, Diagnostics, and Action Recommendations

PARC’s System Sciences Lab (SSL) is unlike any organization in the world. Combine the best of an esteemed research organization, a top notch university, an entrepreneurial startup, and a premiere technology company and you get SSL, a team that melds its world-class innovation chops with a focus on researching and developing real world applications that have market potential.

The aim of this project is develop an advanced framework that integrates the capability of automatic feature learning provided by machine learning with the physics-based and the domain knowledge-based feature selection methods.

If you are eligible to apply for this research studentship, please complete the online application form which can be found at:

https://www.cranfield.ac.uk/research/phd/intelligent-feature-extraction-and-selection-framework-for-fault-diagnosis-and-prognosis

Many thanks,

Dr Zakwan Skaf

IVHM Centre
Cranfield University

As a research engineer you integrate top technology into the assembly line of manufacturing companies, optimizing human-robot collaboration setups in the work cells.

In machine assembly collaborative robots are of great value due to the high precision requirements and complex procedures. Even more so in the context of “flexible assembly”, where companies have to meet a climbing demand for tailor made products.

Do you love machine learning and data analytics? Do you dream about new regression algorithms?

We’re currently implementing the art of the possible in prognostics, diagnostics, anomaly detection, optimization and more ‘run of the mill’ analytics. It certainly aint easy.