21 virtualization "https:" "https:" "https:" "Universitätsklinikum Hamburg Eppendorf" PhD positions at Cranfield University
Sort by
Refine Your Search
-
industrial partners, such as WAAM3D (https://waam3d.com/ ) and members of WAAMMat (https://waammat.com/ ), gaining valuable industry experience and exposure. The student is expected to acquire the following
-
research community at WAMC, fostering collaboration and innovation. Additionally, there will be opportunities to work with WAMC’s industrial partners, such as WAAM3D (https://waam3d.com/ ) and members
-
academic and industrial. Please check the hub website for further details: https://www.liverpool.ac.uk/energy-transfer-skills-training-hub
-
stoppages. However, in most of the industries, OEM specifications are used to maintain the equipment’s/machineries which are often based on schedules and preventive strategies. It has been observed
-
addressed this problem through non-intrusive particle image velocimetry (PIV) to measure the unsteady velocity field. This provides an increase in spatial resolution of about 2 orders of magnitude relative
-
experts in the prognostics and condition monitoring field, as well as being part of our strong and dynamic research centre at Cranfield University. About the host University/Centre Cranfield is an
-
. Although there is a clear synergy between fatigue damage and corrosion, most fatigue prognosis models do not explicitly consider the role of the environment, which is usually reduced to obscured fitting
-
image correlation is an effective tool to characterize material properties. The analysis of the images can provide a fair assessment about the changes in material behaviour under different operational
-
diagnosis and prognosis technologies, and, consequently, improve maintenance decision making. Currently, machine learning exists as the most promising technologies of big data analytics in industrial problems
-
mechanism. The integrating should enable to guarantee certain properties of the learned functions, while keep leveraging the strength of the data-driven modelling. Most of, if not all, the traditional