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Image Processing Engineer

1 month ago

Sydney, New South Wales, Australia Kinexus Full time

We're seeking candidates who are highly skilled Image Processing Engineers. The role is to help R&D image capture and management on cutting edge systems like ISR camaras and FLiR capabilities, in support of defence and government security operations here in Australia and further afield. Candidates will be responsible to develop cutting-edge algorithms for next-gen electro-optical systems/sensors.

Your role will be to work across image processing, computer vision, and sensor fusion to enable automatic target recognition capabilities including detection, classification, and tracking. Candidates will also develop the technologies to optimise low level image enhancement to round out the definition or future R&D needs. This role is crucial to enhance capabilities that will assist in offensive and defensive operations for the ADF.

Responsibilities

  • Design and implement image processing algorithms using MATLAB and Python for prototyping
  • Develop features like object detection, classification, and target tracking
  • Optimise image enhancement techniques (e.g. denoising, sharpening, filtering)
  • Integrate solutions with broader optronics systems
  • Contribute to system testing, documentation, and R&D roadmaps

Ideal Candidate

  • Degree in Electrical Engineering, Computer Vision, Signal Processing, or related field
  • 5+ years in image/signal processing (defence, aerospace or R&D sector preferred)
  • Proficient in MATLAB and Python
  • Experience with EO/IR cameras, gimbals or UAV payloads, tracking, image registration and enhancement
  • Knowledge of embedded systems or GPU-based deployment
  • Strong communication and problem-solving skills

Desirable

  • AI/ML experience (TensorFlow, PyTorch)
  • Real-time/embedded implementation knowledge
  • Experience managing R&D collaborations

All applications should include your resume as a word document. For more information, visit our website, or contact Eirene Andre on #LI-EA

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