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The candidates will design, develop and measure multi-qubit devices, where they will learn a wide range of techniques from single atom manipulation, device processing and optical measurements
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The successful applicant will conduct research to design and develop novel machine/deep learning based trust technologies for securing IoT services/devices. The successful applicant will conduct
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degree with strong skills in programming and machine learning. Please contact Zhuang Li for more information. The project focuses on developing multilingual datasets and advanced methods to detect and
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completed a relevant Bachelor’s Degree and Honour’s or Master’s. Desirable criteria: Practical experience and conceptual background in cellular immunology. Interest in, and ability to, learn bioinformatics
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computer vision and machine learning methods to interpret the photovoltaic (PV) solar farm's condition and perform various inspections and anomaly detection. The research will draw from state-of-art
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@rmit.edu.au Dr. Shao, Wei (Data61, Marsfield) - wei.shao@data61.csiro.au The successful candidate is expected to have strong motivation and evidenced skills in machine learning and computer vision
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in, and ability to, learn bioinformatics. Experience in tissue culture. Applicants need to have a background in immunology, genetics or bioinformatics. They must have completed a relevant Bachelor’s
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following essential criteria: Qualifications and/or professional experience in one or more of: social science, ecology/conservation or environmental economics A demonstrated capacity to implement or learn
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of the internationally recognised Integrated Photonics and Applications Centre working with a team of more than 50 people at RMIT University and beyond. Through this project you will learn about both integrated optics
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(sinclair.davidson@rmit.edu.au) Applicants are advised to contact Sinclair Davidson (sinclair.davidson@rmit.edu.au) Would suit applicant eager to learn more about the cryptoeconomy. Would suit applicant eager to learn