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magdalena.plebanski@rmit.edu.au . A copy of electronic academic transcripts A CV that includes any publications/awards and the contact details of two referees To apply, please submit the following documents
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) scholarship is available. First class honours. First class honours. All applicants should email the following to Professor Anna Hickey-Moody, anna.hickey-moody@rmit.edu.au and Dr Stephen Gaunson
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PhD Scholarship in Integrated Photonics for Telecommunication, Biosensing and Precision Measurements
Bachelor/Masters degree (or equivalent) in electrical/electronic engineering, nano/microfabrication, physics (optical) or a field related to the desired skills listed below, with a high level of academic
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. Magdalena Plebanski via magdalena.plebanski@rmit.edu.au A copy of electronic academic transcripts A CV that includes any publications/awards and the contact details of two referees. To apply, please submit
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submit the following documents to Dr. Sara Baratchi via sara.baratchi@rmit.edu.au A cover letter (research statement) A copy of electronic academic transcripts A CV that includes any publications/awards
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of surfaces has profound effects on morphology and composition as established by supervisor teams. When ultra-short laser pulses are used, highly energetic electrons leave the laser exposed region with ions
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. Magdalena Plenbanski via magdalena.plebanski@rmit.edu.au and Dr April Kartikasari via april.kartikasari@rmit.edu.au . A copy of electronic academic transcripts A CV that includes any publications/awards and
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students may be considered subject to the approval of the Fight Food Waste CRC High quality applicants with relevant background to email their Resume and Application letter to Associate Prof Asgar Farahnaky
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. Candidates should have an undergarduate degree in a relevant field of science or engineering. Please contact Dr Philipp Reineck via email: philipp.reineck@rmit.edu.au Please contact Dr Philipp Reineck via
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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