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Australian National University | Canberra, Australian Capital Territory | Australia | about 2 hours ago
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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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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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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researcher with a PhD in Computer Science or a related field, experienced in machine learning for spatial data management, with a track record of publications in top-tier venues such as SIGMOD, VLDB, ICML
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Skills and Experience A PhD in a relevant discipline (e.g., artificial intelligence, data science, statistics, computer science, learning engineering, learning sciences, learning analytics, or educational
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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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software and advanced computer skills. Demonstrate the ability to learn new techniques quickly and deliver work in a timely manner. Have experience with advanced mass spectrometry platforms, particularly
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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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. The candidate will have a PhD in Computer Science or Machine Learning (or be able to demonstrate equivalent research experience) and possess a deep and demonstrable knowledge of these fields. They must be a