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Field
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TRAINING: Literature review on anomaly detection in network data; Using deep learning to detect anomalies in network data flows.; 4. REQUIRED PROFILE: Admission requirements: Degree in Computer Engineering
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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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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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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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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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damage in composite repairs due to environmental degradation and mechanical loading. This research requires deep understanding of analytical and experimental ultrasonic NDT methods, demonstrated knowledge
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-cultural and interdisciplinary team Adaptable and willing to enter into deep learning in new areas Fuent in written and spoken English with good communication skills Hiring Institution: LKC
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optimization of multi-modal LLMs. Investigate and implement methodologies to ensure AI authenticity, accountability, and the integrity of digital content. Develop and refine machine learning and deep learning
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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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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