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Australian National University | Canberra, Australian Capital Territory | Australia | about 13 hours 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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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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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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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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(PhD entry level $105,518 p.a.) Join a collaborative and cutting-edge research environment working with world-class researchers. Apply statistics, bioinformatics, and machine learning methods to analyse
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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