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-driven models to optimize solvent systems. Mentoring the training and learning of PhD and undergraduate students on sustainable biomass conversion topics Assist in report/document/further grant writing
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at ANU. We are a transformative centre for research in artificial intelligence and machine learning, computer systems and software, and theoretical foundations of computing. We span traditional and modern
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works to interpret genomes and distill the immensely complex networks that form the foundation of human biology and disease, through accurate machine learning models. Current areas of interest include
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 2 months ago
at ANU. We are a transformative centre for research in artificial intelligence and machine learning, computer systems and software, and theoretical foundations of computing. We span traditional and modern
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mathematical modelling tools. Excellent knowledge of programming languages such as R, Python, Julia, etc. Familiarity with AI algorithms and Machine Learning Fluent oral and written communication skills in
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the following training will be considered PhD in computer science, machine learning, AI or related computational field, or, Ph.D. in a health-related discipline with experience in experimental science, devices
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/mathematical-cognition-and-literacy . The group collaborates with national and international experts in mathematical cognition, mathematics education, linguistics, and machine learning. Your immediate leader
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experience in IC design/Electronics Familiar with Analog/Power Management/RF/ADC/DAC IC design Proficiency in Cadence Proficiency in Python and Matlab Experience in implementing algorithms for machine learning
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, Julia. Understanding of one or more of the following areas would be an advantage Optimization Dynamic systems Control theory Power electronics Signal processing Machine learning and data science (various
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development of future proposals for funding, into AI for renewable energy. You will consider ways in which the integration of machine learning algorithms might support the wider integration of, and uptake