301 phd-in-computational-mechanics-"UNIVERSITY-OF-STRATHCLYDE" PhD positions in Australia
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team at Swinburne University’s Department of Mechanical and Product Design Engineering. This PhD will provide a unique opportunity to conduct impactful research with real-world industry applications
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materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for
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Scholarship in CSIRO Industry PhD Program - Project 1: Resilient & Practical Quantum-Safe Threshold Cryptography Job No.: 678541 Location: Clayton campus Employment type: Full-time Duration: 4-year
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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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PhD. The duration includes an extension of up to six months (PhD) if approved for your candidature. This is the full-time, tax exempt rate which will index annually. You will also receive an additional
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-performance computing (HPC), Bragg Crystallography facility, and Adelaide Proteomics Centre. Our collaboration with Monash University further extends our access to advanced 300 kV Titan Krios and Helios 5 UX
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These supplementary scholarships (top-ups) are funded by the Australian Research Council to support 2 full-time PhD students who wish to undertake research in the field of battery recycling: Project
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program. It is a testament to his desire to support Hellenic studies and research for and by students enrolled in Greek Studies at La Trobe University. As an applicant, you should have an interest in Greek
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to three years of study (with the potential for extension) Scholarship Details Maximum number awarded 1 Eligible courses The successful candidate must enrol into a Doctor of Philosophy (PhD) program
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models