669 postdoc-in-distributed-systems-and-controls-"Multiple" positions at Monash University in Australia
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at pandemic levels, understanding how to prevent these issues from developing in adolescence is critical to international global health. This project will build on the ideas and concepts from Positive Youth
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Dowe, 1999a) ensures that - at least in principle, given enough search time - MML can infer any underlying computable model in a data-set. A consequence of this is that we can (e.g.) put latent factor
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. More recent advances include the increasing emergence of driverless cars. This is an area in which rapid changes continue to occur, most recently as the world both deals with and looks to emerge from
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promising results in building prediction models, they are typically data-centric, lack context, and work best for specific feature types. Interpretability is the ability of an ML model to identify the causal
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Anomaly detection is an important task in data mining. Traditionally most of the anomaly detection algorithms have been designed for ‘static’ datasets, in which all the observations are available
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known as Team COMPAS -- includes a number of amazing undergraduate and graduate students, postdocs, alumni, and other fantastic collaborators. Please contact me if you are interested in joining our group
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determined by combining the observed space density of galaxies, the measured spatial distribution of galaxies and simulations of the dark matter distribution. Example themes for student projects follow and
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psychopathological features of the illness, the moderate efficacy of existing treatments suggests we do not yet have a full understanding of what is core to these illnesses, nor how best to heal from them. This PhD
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a
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Species’ distributions are shifting in response to global climate change and other human pressures. Accurate methods to monitor and predict distribution shifts are urgently needed to manage