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of data analysis techniques (including machine learning) and process-based algorithms to diagnose and interpret high-frequency hydraulic data for the purpose of providing actionable information. Analysis
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. This position will involve the development of data analysis techniques (including machine learning) and process-based algorithms to diagnose and interpret high-frequency hydraulic data for the purpose
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. Learn more about Monash . The Opportunity The School of Electrical and Computer Systems Engineering located within the Faculty of Engineering has an exciting opportunity for a Postdoctoral Research Fellow
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flow cytometry Excellent communication and organizational skills Awareness of confidentiality and privacy principles Computer literacy with Microsoft Office and a willingness to learn new software
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both independently and as part of a team. Demonstrated experience supporting and contributing to projects and initiatives. Highly proficient computer literacy with excellent skills in Microsoft Office
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. Excellent written and verbal communication skills and the ability to liaise effectively with all levels of staff and external stakeholders. Highly proficient computer literacy with excellent skills in
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, Python, Bioconductor, GitHub, and HPC use Experience implementing multi-omics and machine learning in a genomic context Wet lab experience in basic molecular biology and flow cytometry If you're ready to
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Experience in Machine Learning OR human factors, HCI or psychology will be highly regarded. Based at our picturesque and Vibrant St Lucia Campus About UQ As part of the UQ community, you’ll have the
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quality service delivery and you will have well-developed computing skills in using office computer applications. You will also have completed a degree, or equivalent combination of experience and/or
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School of Mathematics and Physics Analytics for the Australian Grains Industry (AAGI) Play a lead role in developing crop trait prediction solutions Utilizes physics-informed machine learning (PIML