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the Department of Materials Science and Engineering (MSE), Faculty of Engineering, Monash University. This PhD project forms part of the Baosteel–Australia Joint Centre (BAJC) collaboration and will investigate
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integrated with, or operate alongside, metal detectors to deliver cutting-edge detection capabilities. Applications of this research include: Detection of subsoil layers, utilities, landmines and IEDs, UAV
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months of their candidature start date (or the equivalent pro rata for part-time students); be progressing satisfactorily in their candidature; not have previously received funding for participation in
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microfluidic fabrication and experiments 3D printing machine learning. Demonstrated programming skills (Matlab, C++, or Python). Desired Demonstrated ability to work independently and to formulate and tackle
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. Scholarship Details Maximum number awarded 1 Eligible courses The student will be required to undertake online courses related to the research topic and corrosion theory, in addition to completing a course on
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group of PhD researchers who will tackle the most pressing questions in Machine Learning while ensuring AI serves humanity responsibly. You'll work within one of our specialised research themes, each
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funding in the form of a Living Allowance Stipend and a tuition offset support to work with their industry partner on the following Australian Research Council [ARC] Linkage Project: Optimising cost
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, advanced soil analysis, and modelling. The research involves field work in Western Australia’s unique ecosystems, applying cutting-edge imaging, spectroscopy and molecular techniques, and leveraging machine
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to apply: Seismic wave propagation theory; seismic data acquisition and processing; signal processing, seismic imaging, geophysical inversion, scientific programming, reservoir modelling. Previous research
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completed a PhD • not be in receipt of another primary scholarship • be able to commence the program in the year of the offer • enrol as a full-time PhD student. The Program is not designed for part-time