Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
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
-
to peer-reviewed academic publications Qualifications Completed undergraduate degree in physics, computer science, machine learning, computational modelling, or similar. About Swinburne University
-
supervisory team from La Trobe University and Sheffield Hallam University, gaining expertise in data-driven performance analysis, statistical modelling and machine learning. This research will enhance our
-
models for structural health monitoring of civil engineering structures. Digital twin models are used to interpret real time information from videos and images aided by computer vision techniques
-
into the learning dynamics and learnt features of neural networks, this research has the potential to significantly improve the interpretability and reliability of AI models. Enhanced interpretability will enable
-
models that can forecast the likely outcomes of current practices. The project aims to develop cutting-edge machine learning and statistical risk prediction techniques to predict each short-term, long-term
-
tools to record and derive important contextual information. The student will also learn relevant statistical techniques such as Linear Mixed Modelling to compare between drills and competition
-
science technologies, and this is a perfect training opportunity for those who is interested in machine learning, data mining, artificial intelligence, and bioinformatics. High-performance computing may