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loading, flexible working arrangements including hybrid on site/WFH options and flexible start/finish times, and genuine career progression opportunities via the academic promotions process. About You Level
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presents an exceptional opportunity for a suitably qualified and motivated individual to engage in applied research at the intersection of artificial intelligence , process monitoring , machine learning
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Australian National University | Canberra, Australian Capital Territory | Australia | about 5 hours ago
Classification: Academic Level B Salary package: $118,632 - $134,507 per annum plus 17% superannuation Terms: Full time, Fixed term (up to 3 years) The Position This postdoc will explore
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Research Fellow, Implementation Science in Addition Treatment Job No.: 683764 Location: Turning Point, 110 Church Street, Richmond Employment Type: Full-time Duration: 12-month fixed-term
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or doctoral qualification in neuroscience or a closely related field, and a strong background in signal processing, programming and electrophysiological techniques. Demonstrated skills in data analysis and
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and MALDI/ESI, and lignin composition using TDA/GCMS. The postdoc will work closely with teams developing engineered plants to develop a deeper understanding of cell wall architecture. This is expected
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translational clinical research. The successful candidate will be responsible for the design, development, and characterisation of a diagnostic electrochemical biosensor, incorporating signal processing and
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in advanced signal processing techniques and good understanding of emerging machine learning methodologies used in NDE. You will work in close collaboration with project partners at the University
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, please refer to 'How to apply for Monash Jobs '. Your application must address the Key Selection Criteria. Please indicate whether you are applying at Level A or B, depending on your experience and
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neurocritical care research The Opportunity We are seeking a Research Fellow - Data Science professional with strong expertise in machine learning, deep learning and high-frequency physiological signal analysis