16 post-doc-in-seismic-groung-response-analyses PhD positions at Monash University in Australia
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Doctor of Philosophy (PhD) or Master of Engineering Science (Research) International Scholarship Opportunities at Faculty of Engineering Location: Clayton campus Employment Type: Full-time Graduate
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Doctor of Philosophy (PhD) or Master of Engineering Science (Research) Domestic Scholarship Opportunities at Faculty of Engineering Location: Clayton campus Employment Type: Full-time Graduate
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Scholarship in CSIRO Industry PhD Program - Project 2: Techniques and Frameworks for Enabling Post-Quantum Cryptography (PQC) Migration Job No.: 678538 Location: Clayton campus Employment Type: Full
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Multiple PhD Scholarships available - Cutting-edge research at the frontiers of Whole Cell Modelling
and compounds to create test environments to trigger bacterial responses. Using transcriptomic, proteomic and metabolomic analyses, we will monitor the responsiveness of several bacterial species
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and innovation catalyst, in this exciting project, you will develop novel algorithms to monitor and analyse workers' movements, detect harmful movement patterns, and implement simple intervention
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: Full-time Graduate Research Degrees: E8009 - Doctor of Philosophy (Monash - Bayreuth) Duration: 3.5-year fixed-term appointment Remuneration: AUD 36,063 per annum (tax-free stipend) (2025 rate) Monash
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of Transport Studies, Department of Civil and Environmental Engineering Employment Type: Full-time Graduate Research Degrees: 3291 - Doctor of Philosophy (PhD) Duration: 3.5-year fixed-term appointment
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I supervise computational projects in electron microscopy imaging for investigating materials at atomic resolution. Some projects centre on analysing experimental data acquired by experimental
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: Full-time Duration: Doctor of Philosophy (PhD) - 3.5-year fixed-term appointment Master of Engineering Science (Research) - 2-year fixed-term appointment Remuneration: The successful applicant will
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or unethical responses, can be manipulated into producing them when provided with a large enough number of well-crafted “in-context” examples. This raises critical questions: Why do AI models adapt so