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structural health monitoring, especially on computer vision, image processing, machine learning, deep learning, signal processing and data analysis techniques, are preferred. Application process To apply
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degree with strong skills in programming and machine learning. Please contact Zhuang Li for more information. The project focuses on developing multilingual datasets and advanced methods to detect and
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product to benefit society and the environment. The group's Digitalisation and Computation Stream is led by Dr Camilo Cruz Gambardella who is developing new understandings of AI and Machine Learning tools
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government background checks (allow for between 4 to 8 weeks) and complete any other CSIRO requirements. Selection criteria To be eligible applicants must: Have a basic understanding of machine learning
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broad goals in decision making in contested and dynamic environments, multi-agent reinforcement learning, and secure and robust machine learning solutions. The ideal candidate will enjoy working in a team
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isothermal flow conditions relevant to bubble reactors using optical diagnostic methods, followed by image processing, which may include machine learning-based techniques. To analyse bubble dynamics from
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or climate models. Knowledge of machine learning, AI techniques, and cloud computing for data processing and model deployment. Familiarity with crop models (APSIM, DSSAT, AquaCrop), irrigation systems, and
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fairness, privacy and legal guarantees for ADM systems, such as recommender and machine learning based systems. It takes a multi-disciplinary approach and although focused on the transportation focus area
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@rmit.edu.au Dr. Shao, Wei (Data61, Marsfield) - wei.shao@data61.csiro.au The successful candidate is expected to have strong motivation and evidenced skills in machine learning and computer vision
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, Engineering or others related to the PhD topic) Excellent programming and/or robotics background, with a keen interest in human-robot interaction Prior knowledge of robotics and machine learning (e.g., relevant