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how local phase transformations can be harnessed to design new classes of advanced steels. The research aims to develop a mechanistic understanding of localised transformation behaviour and to design
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: Developing strategies for enhancing Supply chain Resilience by drawing lessons from various disruptions such as Brexit, the US-China trade war, the COVID-19 pandemic, the Suez Canal blockage, the Russia
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AI & Cyber Futures Institute - Charles Sturt University | Bathurst, New South Wales | Australia | 2 months ago
Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Available Scholarships PhD Student Scholarships: Development
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and career development, culminating in a written thesis. The PhD stage will typically take three and a half years to complete. You will also enjoy opportunities for domestic and international conference
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scholarship package totaling approximately $47,000 per annum tax exempt (2025 rate). A four year Project Expense and Development package of $13,000 per annum. A 60 day Industry Engagement component with
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supports the development of clinician-researchers who can bridge the gap between research and practice, strengthen interdisciplinary collaboration, and lead innovation in healthcare delivery across diverse
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materials and we utilise these non-absorbed X-rays to massively increase image contrast and reduce radiation exposure using coherent synchrotron radiation. We have developed these “phase contrast” and “dark
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. National Road Safety Partnership Program (NRSPP) offers a collaborative network to support Australian businesses in developing a positive road safety culture. It’s about saving lives without the red tape
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future autonomous instrument control and self-directed experimentation will be developed, recognizing the challenge presented by the integration of multiple complex systems. Coding and user interface
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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