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» AlgorithmsYears of Research ExperienceNone Additional Information Eligibility criteria - PhD in one of the following areas (or related fields): * Machine learning / deep learning * Quantum computing / quantum
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Vision Profiler (UVP), and to analyse its spatial and temporal variability. This will be done by combining different data sources and machine learning (ML). Data used for this ML approach include - a
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position, available until December 2027. Flexible work arrangements can be negotiated with the right candidate. Be part of the Australian Institute for Machine Learning – the largest computer vision and
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networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis
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, Electrical Engineering, Aerospace Engineering or a related field, with a focus on Robotic Perception and learning based methods Demonstrated expertise in at least one of the following areas: Machine Learning
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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
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industrial conditions. Keywords: Artificial intelligence, autonomy, digital twin, edge computing, UAV systems. Objectives: Support the development of AI and machine learning algorithms for autonomous
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, the Frontier supercomputer, and collaborate with experts in machine learning, optimization, electric grid analytics, and image science. The successful candidate will design and implement differential privacy
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Certificates/Credentials/Licenses NA Computer Skills General office suite and willingness to learn lab specific programs Supervisory Responsibilities No Required operation of university owned vehicles No Does
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Northwestern, TTIC, and UIC. Postdoctoral researchers will conduct research on key aspects of the foundations of data science. This field includes relevant areas of Theoretical Computer Science, Machine Learning