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Integrated Planning & Learning and Reinforcement Learning in non-deterministic and partially-observed scenarios. The methods will be evaluated on physical robots. The ideal candidate would have: A PhD (or
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Australian National University | Canberra, Australian Capital Territory | Australia | about 2 months ago
for uncertainty quantification in learned computer vision. The person should have a PhD in Computer Vision or a closely related field, and a demonstrated strong track record in this field. This should include
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training materials for research teams, focusing on data science and machine learning techniques in geoscience. Position description: PD [Research Fellow] [520112].pdf To learn more about this opportunity
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), clinical trials, disease surveillance, and the use of novel methods including Bayesian network, machine learning, social network analysis and dynamic data visualisation tools. Further information is
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area of expertise. You may be a great fit if: You are a passionate researcher with a PhD in Computer Science or a related field, experienced in machine learning for spatial data management, with a track
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and standing recognised by the University/profession as appropriate for the relevant discipline area (e.g., AI/Machine Learning, Bioinformatics). A proven track record of research and scholarly
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research activities, and engaging in teaching, student supervision, and mentoring in the areas of edge computing and machine learning. About You The successful candidate will hold a PhD in Computer
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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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, virtual screening, molecular docking, structure-activity relationship analysis, and machine learning. Candidates should embrace opportunities to tackle new problems and challenges as part of a dynamic team
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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