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business needs and document business requirements. Develop financial models to support cost-benefit analysis and identify potential risks and recommend mitigation strategies. Work with cross-functional teams
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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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of Machine Learning as the problem of approximating function f from the pair of measurements (x,y), and Optimization as the problem of finding the value of input x that maximizes the output y given
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position is fulltime or part- time (0.7FTE) Based in Hobart, Fixed Term until June 2027. Please note that this is likely to extend beyond, subject to ongoing funding model. In return for your experience
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. Desirable: Proficiency in scientific programming (e.g. Python) and familiarity with data science and machine learning techniques. Experience with geochemical analytical techniques and working in a laboratory
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. They generally rely on expert rules or machine learning models to provide health advice. Recently, generative AI tools, such as ChatGPT, have become a popular focus of research. In healthcare, they show strong
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, complex omics datasets (e.g. transcriptomic, genomic, proteomic), with demonstrated skills in statistical modelling; experience in machine or deep learning is advantageous. Emerging track record of research
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algorithms and deep learning models. Have proficiency in Python in a Linux environment and development experience using Tensorflow or PyTorch. Have strong linear algebra and computer vision knowledge. Have
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while inferring underlying physiological changes. Required knowledge Machine learning, dynamical systems theory, control theory, signal processing, time series analysis, neuroscience are all relevant and
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models for structural health monitoring of civil engineering structures. Digital twin models are used to interpret real time information from videos and images aided by computer vision techniques