261 structural-engineering "https:" "https:" "https:" "https:" "https:" "https:" "Multiple" positions at Monash University in Australia
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, which has a powerful suite of instrumentation, including the Thermo Scientific Spectra-φ, an advanced S/TEM with beam blanker, multiple fast pixelated detectors and unique electron-optical elements
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, including the Thermo Scientific Spectra-φ, an advanced S/TEM with beam blanker, multiple fast pixelated detectors and unique electron-optical elements to optimise performance in S/TEM. It is located
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them The Opportunity Monash Engineering is seeking a proactive Senior Coordinator, Industry Engagement to support the Faculty’s strategic industry partnerships and engagement initiatives. In this role
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intelligence (XAI). This project will build on the methodology of formal explainable AI (FXAI) and aim at advancing FXAI technology and broadening its use by seeking (1) how to efficiently represent an AI system
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investigative skills and knowledge of risk management principles. Exceptional interpersonal and communication abilities to manage sensitive, high-stakes situations. A collaborative mindset to work across multiple
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About the Opportunity The Faculty of Engineering, within the Department of Materials Science and Engineering, is seeking a standout Research Fellow to join our team. In this role, you will play a key part
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progress, delivery and outcomes. Additionally, you will provide structured feedback to management which contributes to the development, delivery and improvement of Access, Inclusion and Success and informs
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standards in animal care. We are looking for candidates who have completed or are working towards a Diploma of Applied Science (Animal Technology) with relevant experience. A passion for animal care, strong
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their understanding of Indigenous knowledge systems and how they support research into human and technology futures, as well as their interest in innovative and participatory methodologies. Applications can be
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testing approaches that can be used to verify that machine learning models are not biased. Required knowledge Software engineering, software testing, statistics, machine learning