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Multi-modal Machine Learning—including areas like Neuro-symbolic AI, Knowledge Graphs, Contextual AI, Conversational AI, and Trustworthy & Safe AI. This role also offers the opportunity to explore human
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Research Areas and Capabilities: Digital Engineering, Artificial Intelligence (AI) and Human Machine-Teaming Aerostructures and Flight Science Advanced Composite and Alloy Additive Manufacturing Propulsion
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Levin Kuhlmann Research area Machine Learning We are seeking a highly motivated and innovative PhD student interested in exploring the opportunities for using AI to enhance personalisation of services and
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mechanical loading of such samples. The focus of the PhD project will be to use machine learning techniques to better understand the interplay between the crystal orientations and deformation patterns in a
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the toxicity of degradation by-products. To be successful in this role, you will hold (or be near completion of) a PhD in chemistry, materials science, chemical engineering, or a related field. You will have
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This PhD project is part of a larger project that aims to explain the uncertainty of Machine Learning (ML) predictions. To this effect, we must quantify uncertainty, devise algorithms that explain
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life with a strong sense of community & inclusion Enjoy a career that makes a difference by collaborating & learning from the best At UNSW, we pride ourselves on being a workplace where the best people
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facilities. About you Have a PhD in a field relevant to extragalactic astronomy. Demonstrated commitment in participating in high quality research evidenced by peer-reviewed journal articles published in
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of mining operations. Digital innovation is playing a key role in this transformation, with developments like automation, predictive analytics, artificial intelligence and machine learning, allowing
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on site/WFH options and flexible start/finish times, and genuine career progression opportunities via the academic promotions process. About You Completed PhD or equivalent in Design or equivalent