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University of Western Sydney | The University Of Sydney, New South Wales | Australia | about 1 month ago
Investigators from the Powerhouse Museum. Additionally, you will engage in high-impact research using deep neural networks, behavioural and neuroimaging experiments, and contribute to scholarly publications and
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an opportunity for a Postdoctoral Fellow. You will contribute to UNSW’s research efforts in developing machine learning and deep learning algorithms for dynamic systems (sequential or time-series data). Experience
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written communication skills in English for research work Preferred Requirements: Experience with deep learning and reinforcement learning algorithms in robotic decision. Familiar with UAV kinematics and
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of statistics and mathematics are required. Training experience in artificial intelligence (such as machine learning and deep learning), experience working in a high-performance computing cluster environment, and
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able to apply their deep scientific knowledge to solve complex real-world problems, and work with stakeholders to understand their needs and adjust and communicate their approach accordingly to meet any
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, Nutritional Sciences and Women's Health cluster) for REF was rated as world-leading or internationally excellent. We use this expertise to teach the next generation of health professionals and research
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should be highly proficient with a range of machine learning approaches, including unsupervised, semi-supervised, supervised, and various deep learning techniques (e.g., convolutional neural networks
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Requirements: PhD degree in Artificial Intelligence, Computer Science, or a related field from a prestigious institution. Good written and oral communication skills. Proficiency in developing deep learning
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system, including traffic prediction, optimization, and safety enhancements. Key Responsibilities: Conduct applied research in machine learning, deep learning, and reinforcement learning for optimizing
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and synthesis of novel materials. Key Responsibilities: Develop and apply generative AI models for materials discovery, leveraging deep learning, Bayesian optimization, and active learning. Integrate