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of student researchers and outreach Job Requirements: PhD degree in Materials Science, Materials Chemistry or Chemical Engineering Proficiency in either Diamond Anvil Cell (DAC) high pressure experiments or
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following fields: Large random matrices Machine Learning Job Requirements: PhD degree in statistics or related areas. Experience in random matrices or machine learning Strong written and verbal communication
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to the research program. Job Requirements: A PhD in Computer Science or relevant fields. Strong background in deep learning and neural rendering. Excellent programming skills to implement and deploy deep learning
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duties related to the research program. Job Requirements: A PhD in Computer Science, Quantum Computing, or relevant fields. Strong background in quantum computing and quantum machine learning. Strong
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to the research program. Job Requirements: A PhD in Computer Science or relevant fields. Strong background in deep learning and multi-modal learning. Strong publication record in top conferences/journals, such as
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learning (reinforcement learning, multi-armed bandits). This position is expected to last from one to two years. The start date is negotiable. The candidate is expected to have a PhD in electrical
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deep learning to work with a team from multiple disciplines of machine learning and artificial intelligence to develop multimodal large language models with capabilities such as conversational
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and transfer learning) for battery management for rugged systems that lead to publications in top-tier international conferences and journals, as well as real-world implementations. If interested
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The appointed candidates will support the planning and execution of research projects alongside postdoc associate, PhD students, and RAs within the research group and the Centre for 5G Digital Building Technology
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The appointed candidates will support the planning and execution of research projects alongside postdoc associate, PhD students, and RAs within the research group and the Centre for 5G Digital Building Technology