Power Efficient Deep Learning Models for Environmental Monitoring at the Edge
This is an exciting opportunity to showcase how deep learning can benefit Antarctic research and remote biological monitoring. Working in the Securing Antarctica's Environmental Future (SAEF) program, this ARC-funded, multi-disciplinary PhD project aims to develop a set of novel low power, extremely accurate, deep learning algorithms to process a stream of data coming from various sensors, including a camera, deployed in Antarctica and extract (visual) information about the environmental conditions (moss he
Faculty: Faculty of Engineering and Information Sciences, Faculty of Science, Medicine and Health
Study area: Computer Science & Information Technology, Engineering, Environmental & Biological Science, Maths
Student type: Domestic students, International students
Scholarship amount: Successful candidates will receive a stipend of $34,000 p.a. for 4 years (or full-time equivalent).
Application closing date: 8 December 2024