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Field
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Researcher in FPGA-based AI Hardware Acceleration who has: strong experience in FPGA design, machine learning or a related field in the case of the Postdoctoral Research Associate, a PhD (or near completion
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Acceleration who has: strong experience in FPGA design, machine learning or a related field in the case of the Postdoctoral Research Associate, a PhD (or near completion) in FPGA design, machine learning or a
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-team and contribute to a clinical trial research project of national significance for youth mental health. The project applies modern data science methods to build explainable and integrated machine
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data science methods to build explainable and integrated machine learning models that can be utilised by health services to make real-time, data-informed clinical decisions in youth mental health care
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has: a PhD in a related or relevant field to this project design and conduct experiments collect and analyse data, including with the use of machine learning develop mathematical models of excavation
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density) influence energy dissipation develop mathematical models to predict and explain these effects collect and analyse data, including with the use of machine learning use this knowledge to design
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density) influence energy dissipation develop mathematical models to predict and explain these effects collect and analyse data, including with the use of machine learning use this knowledge to design
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commercially orientated research projects in computer vision and machine learning. To be successful you will need: A PhD in Computer Science, Engineering or other Machine Learning-related field. Programming
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PhD in Computer Science, Engineering or other Machine Learning-related field. • Programming experience in python, C++ or other relevant language and experience in deep neural networks • Strong
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postdoctoral researcher with: A PhD (or near completion) in Computer Science, Computational Biology, Mathematics, Bioinformatics, or a related discipline. Proven expertise in machine learning and algorithm