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encoded in computer software and can be used as decision support systems (DSS). These may be used by decision-makers with different domains of expertise than the analysts who built the DSS system. Therefore
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Areas covered include wireless communications; intelligent monitoring and surveillance for natural and industrial environments; intelligent embedded systems; decision making and machine learning
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We are seeking a motivated PhD candidate to work on unsupervised music emotion tagging within the broader field of affective computing. The project aims to develop reproducible machine learning
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are: Rapidly identify AMR and predict treatment responses through use of genomics and machine learning in a clinical context Detect healthcare-associated transmission of AMR in real-time and transform outbreak
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for other duties, skills and experience required for this position. Welcome to Bundoora campus – Please click on the video link below: https://f.io/KDo0ceng Benefits 17% employer contributed superannuation
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Advisory System, or data from other implantable or wearable devices. This involves consideration of both feature-based machine learning or data science approaches and neural mass parameter estimation
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. Required knowledge Strong background in machine/deep learning, computer vision, or applied statistics. Solid programming skills in Python and experience with deep learning frameworks (e.g., PyTorch
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are encouraged to apply. Our aim is to create a workforce that reflects the diversity of the community in which we live. For more information, please visit our Diversity and Inclusion page - https
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various skin condition/s. Relevant resources: DOI: https://doi.org/10.1007/978-3-031-43987-2_20 DOI: https://doi.org/10.1007/978-3-031-43907-0_54
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species' distributions. This project harnesses research in ecological and agent-based modelling, machine learning, and AI to increase the predictive power of models of species’ distribution shifts via “data