Program 2: Building domestic talent for South Australia in Industrial AI

AIML is offering competitive scholarships for high achieving students. AIML will also provide scholarships that support students to complete honours or master’s by research degrees.

The current list of AIML Industrial AI scholarships include:

  • - these scholarships support students undertaking their master’s degrees in AI and machine learning.

Previous scholarships include the which supports two exceptional students engaging in advanced research in AI and machine learning in an Honours program of study at the University of ÑÇÖÞÉ«°É, and the Australian Institute for Machine Learning (AIML) Industrial AI Program Scholarship (PhD) which support students undertaking their PhDs in AI and machine learning. 

Industrial AI PhD Scholarship Key Points

Scholarship recipients

Sarah Dickinson

Sarah’s research interests are in space exploration, stemming from her honours research in machine learning using techniques that measure gravitational waves. At AIML, she is supervised by Professor Tat-Jun Chin and the AI for Space Group to analyse lunar craters using satellite position tracking and computer vision technologies.

Oliver’s research will be examining anthropomorphism— the attribution of human qualities in objects—and how humans perceive consciousness when interacting with AI that possesses human-like features. His project is a joint collaboration between AIML and the University of ÑÇÖÞÉ«°É’s School of Psychology, supervised by Professor Carolyn Semmler, Professor Anton van den Hengel, Dr Jon Opie, and Dr William Ngiam.

Ethan’s research focus is on monocular event-only Visual Odometry (VO)—a process that determines the position and orientation of an object, such as a camera or a robot —and Simultaneous Localisation and Mapping (SLAM), a computational method for developing digital maps, in order to create new applications for space operations. Ethan is also supervised by Professor Tat-Jun Chin.

Will’s research focuses on advancing 3D compositional reasoning by developing transformer based models capable of interpreting and generating LEGO structures. At AIML, under the supervision of Professor Anton van den Hengel, Dr Ravi Garg, and Dr Qi Chen, he has explored tokenisation approaches tailored to LDraw text (the open-source CAD format for 3D LEGO assemblies), uncovering strategies which improve a models ability to learn structural patterns in 3D space.

Jialiang's research focuses on the intersection of algorithm design and analysis, combinatorial optimisation, and advanced machine learning techniques. His research will contribute on improving the operational efficiency in the public health sector through algorithmic solutions. His research is jointly supported by AIML and SA Pathology, under the supervision of Dr Mingyu Guo and Dr Weitong Chen.