Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
. Degree and/or knowledge of core chemical engineering disciplines such as reaction engineering, kinetic and process simulation. Excellent written and verbal communication skills. Problem-solving experience
-
PhD Scholarship in Integrated Photonics for Telecommunication, Biosensing and Precision Measurements
will be trained is some of these different skills: Micro/nanofabrication Simulation/design of optical components Integrated photonic circuits Programming skills in Python, C/C++ Theory of electromagnetic
-
. 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
-
Zhongzheng Wang as your proposed principal supervisor, and copy the link to this scholarship web page into question two of the Financial Details section. About the scholarship Background Two PhD positions
-
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
-
. 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
-
copy the link to this scholarship website into question two of the financial details section.
-
to apply for a research degree . In your EOI, copy the link to this scholarship website into Question 2 of the financial details section. About the scholarship The candidate will be supervised by Prof Cheng
-
degree . In your EOI, copy the link to this scholarship website into Question 2 of the Financial details section. About the scholarship The candidate will be supervised by Prof Cheng Yan and an expert team
-
learning in simulated and indoor/outdoor environment. Reasonable results can be achieved in high signal-to-noise ratio environments; further research is required to improve deep learning in fast variation