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opportunity Experience in machine learning and deep learning Proficiency in one or more high-level programming languages such as Python, Java, or R. Strong commitment to interdisciplinary research and a track
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funding. Candidate Requirements The successful candidate will require a proven track record in research fieldwork and historical methodology. In its assessment, the selection committee will prioritise
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the optimisation strategies to enhance the performance of complex machine learning models such as deep learning model and large language model. Applicants need to have strong background and track records of research
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tracking blood glucose responses to meals and lifestyle factors, the project will identify individual patterns and predictors of glucose variability. Using this detailed data, tailored dietary
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need: A PhD in Molecular Biology, Cell Biology, Genetics, Biotechnology, or a related area. Expertise and interest in cancer and/or stem cell biology and associated techniques. Outstanding academic track
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need: A PhD in Molecular Biology, Cell Biology, Genetics, Biotechnology, or a related area. Expertise and interest in cancer and/or stem cell biology and associated techniques. Outstanding academic track
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want to be, please apply! To be successful you will need: A PhD in genomics or stem cell biology A working knowledge of tissue culture Experience in functional genomics techniques A solid track record of
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look for you, a motivated and talented individual eager to gain experience in the sustainability industry in a defined timeframe. This internship allows you to sharpen your skillset and build a track
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learning by using Bayesian learning principles. Among other things, Bayesian learning gives AI systems the ability to quantitatively express a degree of belief about a prediction or statement. By bridging
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Australian Research Council (ARC) Funded PhD Opportunity at Faculty of Engineering: High-Speed Rail and Sustainable City Sizes in Australia Location: Clayton campus Department/Unit: Monash Institute