Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- RMIT University
- Curtin University
- Monash University
- University of Adelaide
- Queensland University of Technology
- La Trobe University
- Swinburne University of Technology
- University of Southern Queensland
- Flinders University
- Murdoch University
- University of Melbourne
- CSIRO
- Data61 PhD Scholarships
- Nature Careers
- The University of Newcastle
- 5 more »
- « less
-
Field
-
degree with strong skills in programming and machine learning. Please contact Zhuang Li for more information. The project focuses on developing multilingual datasets and advanced methods to detect and
-
affect surface outcomes, benchmark against conventional techniques, and evaluate performance of the finished components. You’ll also delve into intelligent automation and machine learning to optimise
-
what we can learn from this to address image-based sexual violence more effectively in the contemporary media landscape; or examines the relationship between industry and regulatory institutions
-
completed a relevant Bachelor’s Degree and Honour’s or Master’s. Desirable criteria: Practical experience and conceptual background in cellular immunology. Interest in, and ability to, learn bioinformatics
-
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
-
publications and research experiences in structural dynamics and structural health monitoring, especially on computer vision, image processing, machine learning, deep learning, signal processing and data
-
and hands-on experience with AI and computer vision. Solid programming skills in Python, especially with PyTorch. Practical experience with deep learning projects, including working with attention
-
structural health monitoring, especially on computer vision, image processing, machine learning, deep learning, signal processing and data analysis techniques, are preferred. Application process To apply
-
@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
-
stigma and do not reify differences. Failing to apply what we have learned from this approach risks us failing to achieve virtual elimination for all. Aims This research project aims to identify pathways