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methods dealing with model complexity - e.g., AIC, BIC, MDL, MML - can enhance deep learning. References: D. L. Dowe (2008a), "Foreword re C. S. Wallace", Computer Journal, Vol. 51, No. 5 (Sept. 2008
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dynamic environment. Demonstrated advanced computer literacy, including proficiency in Microsoft 365 applications (Outlook, Word, Excel, PowerPoint and Teams), together with experience using travel booking
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are at the end of life. About You A tech savvy individual, you are proficient with a range of computer applications, information systems, and adept to learning new technologies, enabling you to troubleshoot and
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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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with cross-pollination of plants. Experience with genomic, transcriptomic, proteomic and/or metabolomic data analytics Experience with R language and/or computer coding. For full details about this role
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WiFi firmware and drivers, supporting innovative applications in IoT and telecommunications. The successful candidate will be based within the School of Electrical and Computer Engineering (ECE
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information sets Strong computer skills with proficiency in using the Microsoft Office products – Windows, Excel and Word About UQ As part of the UQ community, you’ll have the opportunity to work alongside the
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databases. Experience working with a range of computer systems and applications, including research applications such as NVIVO, SPSS, REDCap, referencing software (EndNote) and commonly utilised programs
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healthcare setting, with a strong focus on reception and administrative functions High level proficiency in computer systems, including Microsoft Office and practice management software Strong organisational
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accurate and consistent advice, particularly in relation to policies and procedures. Proficiency at an advanced level using student administration systems, computer software packages for word-processing and