16 condition-monitoring-machine-learning Fellowship positions at The University of Queensland
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to quickly acquire it. Familiarity with advanced statistical techniques (e.g. GAMLSS), or capacity to gain this knowledge rapidly. Proven ability to publish research, write technical reports, and communicate
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techniques such as PCR, modular cloning, Golden Gate assembly, USER assembly, and CRISPR/Cas9-based genome editing; Sound understanding and practical experience applying the Design-Build-Test-Learn (DBTL
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for the manufacture of computer chips. The project is supported by the Australian Research Council Linkage Project “Innovative Double Patterning Strategies for Integrated Circuit Manufacture” and is within
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You Completion (level A and B) or near completion (level A) of a PhD in the field of Information Retrieval, Natural Language Processing, or Machine Learning on Textual Data. Demonstrated expert
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, or soil science. Demonstrated expertise in gene expression profiling using real time PCR or high throughput sequencing. An understanding or ability to acquire knowledge of plant microbe interactions with a
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expertise in other areas, as well as be open to learning as the project develops. For further information, please view the information booklet. If you are unable to access this link, please email talent