307 phd-studenship-in-computer-vision-and-machine-learning Fellowship research jobs in Australia
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the past five years (2020 or after), or PhD within the past two years (2023 or after) Completion of a research component in an Honours, Masters or PhD program within a creative arts/performing arts
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), or Masters degree within the past five years (2020 or after), or PhD within the past two years (2023 or after) Completion of a research component in an Honours, Masters or PhD program within a creative arts
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data, spatial modelling, multivariate statistics and/or machine learning, and relevant coding languages (e.g. R, Python), including a sound understanding of FAIR data principles, data management and
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neurocritical care research The Opportunity We are seeking a Research Fellow - Data Science professional with strong expertise in machine learning, deep learning and high-frequency physiological signal analysis
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to their culture and pay our respects to their Elders past and present. View our vision towards reconciliation . Role highlights Support research that pushes the boundaries of eDNA science Join a multidisciplinary
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to work independently and collaboratively. Advanced planning, time management, and written communication skills are essential, along with proven computer literacy and proficiency in relevant software and
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sampling missions design of machine learning systems for real-time obstacle detection, terrain analysis, and environmental adaptation in extreme environments implementation of multi-constraint optimisation
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Control” (DP250101137). The Postdoctoral Research Fellow is expected to undertake research on control theory and machine learning techniques for project DP250101137. The Postdoctoral Research Fellow will
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learning materials, mark assessments, and supervise student projects. The role includes mentoring students, supporting research staff, and contributing to School operations and outreach initiatives
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. Desirable: Proficiency in scientific programming (e.g. Python) and familiarity with data science and machine learning techniques. Experience with geochemical analytical techniques and working in a laboratory