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relevant PhD or equivalent qualification/experience in a related field of study, and will have experience in the coding and development of RShiny apps. The successful applicant will be a nationally
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and deploy advanced deep learning and foundation models for surgical scene understanding segmentation, tracking, and operator assistance. You will write, test, and optimise Python and C++ code for real
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the University and with external collaborators. Support project coordination activities, including documentation of research workflows, version control of code and data, and supervision or mentoring of research
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, principles and theories. Develops tools and code to conduct advanced analyses of ocean and atmosphere datasets. Adheres to University and unit-level policies and procedures and safeguards University assets
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Torres Strait Islander people, and people of all abilities, cultures, sexual orientation, and genders. Pre-employment safety and suitability checks: In accordance with the National Higher Education Code
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coding knowledge as these are transferrable to R. The candidate will be working with the Principal Investigator(s) on the analysis of national health datasets, utilising an array of methods to infer
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and robustness across varying noise conditions. Familiarity with PAM workflows and tools and/or marine bioacoustics is required. Strong Python skills, ability to document code and workflows, and
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: Contribute code in Python (and/or C++/JavaScript), work with APIs, and support cloud-based workflows (Google Cloud/Azure experience is a plus). Documentation and communication: Maintain clear technical
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of attoseconds to femtoseconds. The project will involve working with first principles time dependent density function theory (TD-DFT, as implemented in the Elk code) [1-3]. A background including experience in
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, Mechanical Engineering, Computer Science, or a related field for biomechanics and assistive robotics is required by the time the position starts. Additional Qualifications Some background in coding, controls