Research Assistant

About the Role

The Research Assistant will work on photonics and integrated photonic systems, including on-chip optical technologies and light-matter interaction at micro- and nanoscale levels.

The role is part of the ARC E2Crop Hub and the Centre for Atomaterials and Nanomanufacturing at RMIT University. These programs focus on developing technologies for energy-efficient systems, renewable energy integration, advanced materials, sensing, and sustainable production.

The position involves supporting research that spans both fundamental studies and applied outcomes, with applications in energy systems, environmental monitoring, advanced manufacturing, and controlled environment agriculture.

About You

• Proficiency in programming languages, including Python, C++, or Java, focusing on algorithms and data structures applied explicitly in computer vision projects
• Knowledge of machine learning and deep learning frameworks, including TensorFlow or PyTorch
• Knowledge of neural networks - especially convolutional neural networks (CNNs)
• Understanding of image processing techniques - including filtering, edge detection, and image segmentation- for manipulating images
• Familiarity with computer vision libraries, like OpenCV or Dlib, for developing and implementing vision-based models
• Familiar with 3D refractive imaging technique with demonstratable track record.
• Mathematical skills in linear algebra, calculus, and statistics for algorithm development and data analysis
• Evidence of research output including high quality publications, conference contributions and/or technical reports in the field.
• Ability to generate alternative funding projects through effective liaison with industry and government.
• Ability to work autonomously whilst displaying a strong commitment to work in a team environment, including the demonstrated ability to confidently and effectively work with colleagues, project team leaders, and industry partners
• Demonstrated ability to meet deadlines and effectively manage varying workloads and respond to changing priorities as required. Demonstrated high level of communication skills.

Qualifications

Mandatory:

• Ph.D. degree in Optical engineering, Applied Physics, or Computer Science
• Problem-solving abilities for determining innovative solutions to the complex problems common to computer vision engineering - such as dealing with messy background or addressing issues with object detection.
• Attention to detail for ensuring precision in coding and model training - and enhancing the accuracy and reliability of computer vision applications.
• Collaborative skills for working effectively with data scientists, software developers, and project managers.
• Adaptability, including a willingness to stay updated with the rapidly evolving field of computer vision and learn new techniques and technologies as they emerge.
• Communication skills for explaining technical project requirements, progress, and outcomes to non-technical stakeholders