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Level A - $83,468 - $113,262 p.a. plus 17% super Conduct independent research / Collaborate with leading academics / Publish in prestigious journals Apply now to drive innovation in machine learning and
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the integration of cutting-edge technologies, including imaging and video analysis, sensor technology, IoT devices, biochemical sensors, and machine learning. The project’s primary goal is to develop sustainable
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Australian National University | Canberra, Australian Capital Territory | Australia | about 13 hours ago
approaches to model uncertainty for learned computer vision systems, including dense prediction. The position will develop novel methods for deep learning in computer vision that accurately quantify their own
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Australian National University | Canberra, Australian Capital Territory | Australia | about 13 hours ago
intelligence (AI), machine learning (ML) and vision, natural language understanding, and robotics, to build autonomous systems that can perceive, plan, and respond to their environment in pursuit of high-level
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contribute to cutting-edge research in learning sciences, artificial intelligence, quantitative ethnography, and learning analytics across various educational environments, including K-12 and higher education
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requires strong technical skills in Python, R, machine learning models, cloud computing, edge computing, and FPGA. Additionally, you will contribute to the AI Centre by designing new AI subjects, supporting
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of Chemistry is looking for a Post Doctoral Research Fellow to join in an exciting project Parameterisation of voltammetry in a machine learning environment. The project involves working with a multidisciplinary
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. Experience with bioinformatics tools and libraries for genomics analysis (e.g., Seurat, Scanpy, CellRanger, Nextflow, Singularity, Docker). Expertise in machine learning techniques and deep learning frameworks
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of decision making and machine learning, with real-world testing and feedback. The successful applicant will work on ideas in decision making supported by multi-agent reinforcement learning and other related
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collaboratively with colleagues from multidisciplinary disciplines Excellent time management and planning skills, with a commitment to delivery Strong background in machine learning and/or deep learning, and signal