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large data sets or working in applied evaluation projects Experience working with Culturally and Linguistically Diverse communities would be well regarded Demonstrated experience in manuscript and
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the Faculty, the Department of Electrical and Computer Systems Engineering (ECSE) offers internationally acclaimed programs in power systems, telecommunications, electronics, robotics and biomedical engineering
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professional and academic record. An outstanding and demonstrated track record of relevant academic experience, including developing and leading an innovative research program. Demonstrated commitment
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professional and academic record. An outstanding and demonstrated track record of relevant academic experience, including developing and leading an innovative research program. Demonstrated commitment
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they will need in an increasingly disrupted and challenged world. The Research Fellow will be located in the MAIC-QUT Road Safety Research Collaboration (MQ Collab), a three-year research program focused
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 11 days ago
of the key disciplines necessary to help us design, build, regulate and secure the future. CSS brings together expertise in social, technical, computational, ecological and scientific systems to build a new
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basic computational skills. Experience in advanced characterisation techniques (scanning electron microscopy, X-ray diffraction). Knowledge of those techniques and willingness to gain skills in other
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of the key disciplines necessary to help us design, build, regulate and secure the future. CSS brings together expertise in social, technical, computational, ecological and scientific systems to build a new
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. Amandeep Kaur, you will contribute to a vibrant research program centered on the design and development of novel fluorescent probes for super-resolution imaging—a powerful technique revolutionizing how we
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experience contribute to ongoing translational research program related to the application of statistical and machine learning methods in reproductive and perinatal medicine using both clinical quality