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techniques and associated tools (examples include, but are not limited to machine learning, density-functional-theory, materials informatics, finite-element modelling, phase-field modelling), and demonstrated
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on developing and implementing seagrass restoration approaches in tropical Australia and the broader Indo-Pacific region. What you can bring to the role Possess either a PhD, or a Bachelor’s degree with a minimum
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, academic progress and completions. You will oversee the provision of timely and accurate information, advice and service to students and other clients of the STEM College, and have the opportunity to learn
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Chain Analytics, including mathematical modelling, optimisation, and/or machine learning, and/or decision sciences. Advanced expertise in developing supply chain solutions using Python, optimisation
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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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variable models (e.g., CLIP, GLIP, MaskCLIP). Knowledge of Transferability in Machine Learning is desirable. Knowledge in Active Learning is desirable. Programming skills and experience with dataset
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using AI and machine learning in developing new tools for better management of TIC members transformer fleets. Guided by experienced academic staff and supported by Industry experts through the TIC
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, biomedical instrumentation, wearable technologies, biomedical integrated circuits and sensors, neural engineering, optogenetics, and medical machine learning. Quantum Engineering: quantum sciences and
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achieves shared goals and objectives. An in-depth knowledge of Supply Chain Analytics, including mathematical modelling, optimisation, and/or machine learning, and/or decision sciences. Advanced expertise in
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member and contribute to team development Understanding of spatial multiplexing technologies desirable Experience in analysis of pipelines leveraging Machine Learning/AI desirable Experience in the wet-lab