47 condition-monitoring-machine-learning-"Multiple" PhD positions at Cranfield University
Sort by
Refine Your Search
-
control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
-
scintillator-based radiation sensors combining multiple materials with complementary functions, offer a promising route to overcome these limits and achieve unprecedented timing resolution (sub-70ps), enabling
-
AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
. •Specialist training in AI, machine learning, and digital engineering. •Collaboration with academic and industry experts for technical insight and mentoring. •A supportive research environment focused on both
-
, the hydrogen fuel can’t be simply stored within the existing primary structure of the wings but require dedicated tanks. Moreover, due to the cryogenic conditions of the liquid hydrogen on-board, it is also
-
control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
-
are among the world leaders in through-life approaches for high value systems, Condition monitoring, Damage tolerance, Asset management. TES was developed with the support of EPSRC grant of £ 11 million with
-
reusable launchers, autonomous robotics, and advanced materials could redefine how we design space structures. The ability to remotely assemble orbital systems from multiple launcher payloads would allow
-
will use advanced unsteady computational fluid dynamic methods for the analysis of coupled intake/fan configurations in crosswind and high-incidence conditions. The research will adopt these methods
-
relevant to multiple applications, including small aircraft, drones, turbines, and other systems reliant on efficient fluid flow around foils. The project offers a unique opportunity to gain experience in
-
lack a direct correlation with process parameters, limiting their ability to predict temperature fields under varying process conditions. The transferred arc energy distribution becomes particularly