$74,291 to $100,806
- Fixed-term, full-time
- Gardens Point
**Who are we looking for?**
Queensland University of Technology (QUT) is seeking a Research Fellow (Superconductivity /Machine Learning) to join School of Electrical Engineering and Robotics, Faculty of Engineering, Academic Division.
**Real world impact**
QUT is a major Australian university with a global outlook and a 'real world' focus. We are an ambitious and collaborative institution that seeks to equip our students and graduates with the skills they will need in an increasingly disrupted and challenged world.
The School of Electrical Engineering and Robotics plays a significant role in almost every aspect of a modern lifestyle, supplying the energy and intelligence for the technology that helps us to manage and understand the world around us.
We work to ensure safe, affordable and environmentally responsible supply and consumption of electricity. We make sense of the world's data streaming in from cameras, devices, and sensors.
Our expertise in Power Engineering, Renewable Energy Systems, Robotics, AI/Machine Learning, Computer Vision and Signal Processing allows us to develop systems to automatically interpret the world, its people and their activities from diverse visual and audio sources.
We create cutting edge robotic systems that interact with the real world, operating autonomously in our natural and built environments to benefit industries such as power, health, transport, manufacturing, mining and agriculture.
We are committed to the education of Electrical Engineers both as undergraduates and postgraduates, and work with partner Schools to deliver leading edge Renewable Power. AI/Robotics, Mechatronic, Software and Aerospace Engineering education.
**What you need to succeed**
You will also be an active researcher with an exciting and developing research profile that strives to create impact and change and contributes to cutting edge knowledge.
You will demonstrate:
- Completion of a PhD in a relevant discipline preferably in Artificial Intelligence (AI), Machine Learning (ML), Computer Vision (CV) or Signal Processing.
- Experience and research track record in Machine Learning, with experience in one or more of unsupervised deep learning, physics-informed deep learning, or transfer learning/domain adaptation.
- Demonstrated ability to undertake high-quality research via publication in top tier conferences/journals in AI, CV, ML.
- Demonstrated written communication skills, including the ability to maintain accurate records, and experience in summarising scientific data in reports.
- Demonstrated experience in all aspects of the research process, including ethical standards in research confidentiality and an ability to protect intellectual property.