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Postdoctoral Research Fellow Job No.: 675222 Location: Clayton campus Employment Type: Full-time Duration: 12-month fixed-term appointment Remuneration: $80,464 - $109,203 pa Level A (plus 17
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Research Fellow Job No.: 676293 Location: Clayton campus Employment Type: Full-time Duration: 12 month fixed term appointment Remuneration: $80,464 - $109,203 pa Level A (plus 17% employer superannuation) Amplify your impact at a world top 50 University Join our inclusive, collaborative...
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Postdoctoral Research Fellow Job No.: 675222 Location: Clayton campus Employment Type: Full-time Duration: 12-month fixed-term appointment Remuneration: $80,464 - $109,203 pa Level A (plus 17
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, volcanology and geochemistry at more senior levels. Supervision and Mentorship: Supervise Honours and Higher Degree by Research students, as well as postdoctoral scientists, fostering a collaborative and
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well as planetary science. In this role you will develop a significant programme of high quality, impactful and externally-funded research, supervising undergraduate and postgraduate students, and postdoctoral
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delivery applications. We are growing the group to comprise around six postdoctoral staff and six PhD students, forming a highly supportive and talented daily workplace. We regularly utilize large scale
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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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resources to avoid downtime, adjusting dynamically as traffic fluctuates. For researchers and students, this component focuses on developing ML models to predict resource needs, improving load distribution
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such systems are limited to the learning errors due to the neural component. In this Ph.D. project, you will be exploring the use of Lipschitz Continuous Neural Networks to learn Lipschitz-bounded neural models
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such systems are limited to the learning errors due to the neural component. In this Ph.D. project, you will be exploring the use of Physics-Informed Neural Networks to encode the symbolic knowledge