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Field
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advanced machine learning methods for multimodal and 3D medical image analysis in musculoskeletal medicine, in close collaboration with clinicians and computer scientists. PhD or Postdoctoral Researcher
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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data. A core technology leveraged by researchers at the center is deep machine learning, targeting the development of innovative tools and concepts in both the area of molecular biology and the field
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statistical evaluation Machine learning analyses: implementation of established and new workflows Coordination of activities with Consortium partners, including presentation of results at consortium meetings
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and machine learning with Prof. Jason M. Klusowski (https://klusowski.princeton.edu). The position is for one year with the possibility of reappointment based on satisfactory performance and
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rich theoretical and computational environment supported by the Multimodal Language Department. Requirements Essentials PhD (completed or near completion) in Computer Science, Computer Vision, NLP
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engineering capability in machine learning while demonstrating the potential and impact of this knowledge for industries in Australia. To be successful you will need: A completed PhD or a submitted PhD thesis
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motivated post-doctoral associate with a strong background in control systems and machine learning to join the research team of Prof. M. Umar B. Niazi. The position focuses on the development of digital twins
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of quantum computing, quantum algorithms and complexity, quantum cryptography, quantum program verification, quantum machine learning, etc. * Within the predetermined research scope and methodology, conduct
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Institute for Machine Learning – the largest computer vision and machine learning research group in Australia – and contribute to world-leading research projects at the Centre for Augmented Reasoning