Sort by
Refine Your Search
-
demonstrate suitable experience in computer science, machine learning, robotic vision, or a related field (through a high-quality Honours or Masters degree). The successful candidate must be able to enrol as a
-
an ARC Linkage Project focused on developing an autonomous system for detecting and quantifying structural damage in infrastructures (e.g., bridges, grain silos) using computer vision, digital twins, and
-
) use computer vision/machine learning to quantity athlete performance. Develop new computer vision/machine learning methods to enable measurement of sports performance. Research program would make use
-
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
-
these constraints into the training objective, complicating model training. This project aims to leverage advancements in computer vision, particularly in implicit neural representations, to embed priors in neural
-
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
-
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
-
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
-
algorithms and deep learning models. Have proficiency in Python in a Linux environment and development experience using Tensorflow or PyTorch. Have strong linear algebra and computer vision knowledge. Have
-
project management skills. Candidates with strong skillset, including familiarity with structural health monitoring, computer vision and machine learning are desired for this project. Must be eligible