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Field
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, non-parametric methods, machine learning, hierarchical Bayesian modelling, and time- and space-modelling. The group emphasizes general methodological development, often motivated by real-world
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appropriate conditions, it provides a confidence set (credibility set if prediction is Bayesian) for a multivariate estimate with statistical coverage guarantees. This PhD project aims to develop new CP methods
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predictions. To mitigate these effects, advanced ML techniques such as Bayesian deep learning, probabilistic models, and uncertainty quantification methods can be applied to enhance model robustness
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other software's packages. Ability to maintain a day-to-day meeting routine with the group leader to update on the progresses made in the development of these software tools. To be successful at Level B
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the following tasks: Development of new large language models for process modeling Development of new decision-making algorithms Provide regular project updates to principal investigator and funding agency Report
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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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for grant applications. Mentor junior researchers and students, where applicable. Present research work at seminars, workshops, and conferences. Stay updated with the latest advancements in the relevant field
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fellowship. Application Requirements Document requirements Curriculum Vitae - Your most recently updated C.V. Personal Statement USLME Step 1 (if applicable) (Optional) Letter of Academic Standing - A
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Australian National University | Canberra, Australian Capital Territory | Australia | about 7 hours ago
to the production of conference/seminar papers and academic publications, as well as occasional online output for a non-academic audience. You will also participate in the ANU Myanmar Update conference and other MRC
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updates in a landscape evaluation toolkit, which include new models for listed and sensitive species. These models can inform managers during planning on how to improve forest health and reduce adverse