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The scholarship will fund a 3 year PhD candidature to work in the Chemistry Department of the School of Science CAMIC Laboratory. The project is a collaboration with Assoc. Prof Ravi Shukla to test
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1st class Honours in Civil, Environmental or Mechancial Engineering. Please submit a CV, Cover Letter and your academic transcripts to Prof. Aonghus McNabola for initial discussion via aonghus.mcnabola
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to Domestic and International Students. All applicants should email the following to Prof. Xavier Mulet via xavier.mulet2@rmit.edu.au: A cover letter, outlining your interest and any prior research and
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or engineering Strong computer science skills and some experience with statistical, machine learning, and image processing techniques Strong candidates with electrical, mechanical, and biomedical engineering
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This project will examine the behaviour of nanocarriers at cell/bacteria surfaces and response to biological environments and molecules (such as proteins). Research will utilize a suite of cutting edge techniques including confocal, total internal reflection microscopy, AFM, QCM, cryo-TEM and...
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conceptual background in cellular immunology. Interest in, and ability to, learn bioinformatics. To apply, please submit the following documents to Prof. Magdalena Plebanski (magdalena.plebanski@rmit.edu.au
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, bioinformaticians and biologists. Experience in tissue culture. To apply, please submit the following documents to Prof. Magdalena Plebanski (magdalena.plebanski@rmit.edu.au ) and Dr April Kartikasari
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requirement. Open to domestic and international students. All applicants should email the following to Prof. Xavier via xavier.mulet2@rmit.edu.au : A cover letter, outlining your interest and any prior research
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investigations at macro, micro and molecular levels. Modeling tools such as HSC Chemistry and ASPEN Plus will be used to identify most suitable low cost catalysts/minerals and to perform process modeling
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very high resolution, suitable for detecting photovoltaic modules and the cleanliness of solar panels. These images and other data can be processed by computer vision and machine learning methods