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Field
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or more of: the use of micro/nanofabrication and materials characterization tools; computational multi-physics/electromagnetics modelling and/or the application of machine learning algorithms; experimental
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: Develop novel machine learning theories and techniques for analyzing noisy time-series data, with a particular focus on seismic signals Perform uncertainty quantification in time-series analysis to assess
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a role model and fostering an inclusive working culture. Person Specification PhD, or close to completion, in a relevant, quantitative field, e.g. meteorology, machine learning, climate science
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foundations of quantum adversarial machine learning, an emerging field at the intersection of quantum computing and machine learning. It investigates how the unique capabilities of quantum computing can be
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updates to principal investigator and funding agency Report writing/presentation Job Requirements PhD degree in an engineering field related to this project Experience in dynamic modeling, machine learning
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Number: 6480973 Postdoctoral Research Fellow About the Opportunity Job Summary The department of Electrical and Computer Engineering and Institute for the Wireless Internet of Things (WIoT) is seeking a
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optimization of multi-modal LLMs. Investigate and implement methodologies to ensure AI authenticity, accountability, and the integrity of digital content. Develop and refine machine learning and deep learning
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integration and AI models tailored for fish behaviour, health, and stress signal analysis. Investigate and apply novel machine learning and deep learning techniques for pattern recognition, classification, and
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with a PhD in computer science or bioinformatics are encouraged to apply. We create statistical, machine learning, and deep learning approaches for the processing of this data, with a major focus on
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an opportunity for a Postdoctoral Fellow. You will contribute to UNSW’s research efforts in developing machine learning algorithm for photovoltaic applications and utilising them for the investigation