89 parallel-computing-numerical-methods-"Prof" PhD positions at RMIT University in Australia
Sort by
Refine Your Search
-
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
-
Successful candidates will be expected to have a solid background in finance and sustainable finance, research methods, strong data analytic skills, and experience working with large datasets. RMIT
-
This PhD scholarship is for candidates interested in developing culturally inclusive AI by creating methods and datasets to align large language models with global social and legal norms. This PhD
-
plug ins, using mixed research methods to understand user experience, and engaging with the disability community to understand their perspectives toward social aspects of video games. This research seeks
-
between aged care residents, families, and healthcare providers. Using qualitative and participatory research methods, this project will develop best-practice guidelines and policy recommendations to ensure
-
enrolment requirement. Open to Domestic and International Students. Standard RMIT PhD enrolment requirement. Open to Domestic and International Students. All applicants should email the following to Prof
-
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
-
Resume and Application letter to Prof. Asgar Farahnaky: asgar.farahnaky@rmit.edu.au High quality applicants with relevant background to email their Resume and Application letter to Prof. Asgar Farahnaky
-
The primary objective is the development of computational methods and experimental techniques to investigate failure modes and quantify defects and damage in fibre reinforced hybrid composites used
-
This scholarship aims to develop practical methods for optimisaton in large supply chain operations. Ideally candidates should have strong AI, machine learning, and optimisation backgrounds