Sort by
Refine Your Search
-
Category
-
Program
-
Field
-
modelling, enabling more cost-efficient training algorithms. Program overview The successful candidate will receive: Admission to a PhD program at the University of Adelaide; A four-year scholarship package
-
. An outstanding publication record in top tier machine learning and/or computer vision conferences or journals, commensurate with experience and opportunity. The path to Adelaide University We are on an exciting
-
the direction of A/Prof Claudia Szabo in the School of Computer and Mathematical Sciences at the University of Adelaide. The project is a collaboration with Defence Science and Technology Group, within the Combat
-
research unit or comparable environment Ability to provide advice and support to post graduate students Computer literacy including skills in data management, analysis and presentation. Excellent written and
-
collaboration between industry, government, and academia. The Australian Institute for Machine Learning (AIML) at the University of Adelaide is the largest computer vision and machine learning research group in
-
Computer Science, Electrical Engineering, or a related field. A PhD is highly desirable; candidates with a master’s or bachelor’s degree must demonstrate equivalent research or industry experience. Strong
-
. Selection Criteria Level A 1. A PhD in Remote Sensing, Plant phenotyping, Computer Vision, Machine Learning, or a related field. 2. Strong experience in image processing and computer vision, particularly
-
the EEE discipline: Information Systems, Control, Autonomous Systems, and Computer Engineering: Information systems and science, communications, control and dynamics, robotics and automation, artificial
-
the Division of Research and Innovation as part of the co-investment from the University of Adelaide. This program will support ten full-time PhD students commencing studies from 2025 to 2027. In 2025, we
-
8 | Computer Simulations of New Materials for Biotech A major interest of our lab is understanding the unique properties of disordered materials using computer simulations and theory. Our current goal