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Field
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Computer Science, Artificial Intelligence, Mathematics, Engineering, or a related field. Entry level candidates with demonstrated expertise in artificial intelligence (AI), machine learning, deep learning
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experience Required Qualifications: PhD in Electrical Engineering or a closely related field. Preferred: Demonstrated expertise in AI/ML, including deep learning, computer vision, LLMs, VLMs, multimodal AI
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road safety analytics framework. The role involves integrating multi-source transport datasets, developing advanced analytical and machine learning models for risk identification, and supporting
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to conduct research, solve complex technical problems, and develop innovative solutions. Experience with parallel systems, machine learning/AI, or performance benchmarking is highly desirable. Ability to work
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machine learning, particularly at the intersection with control, dynamical systems, and ODE/PDE theory Collaborate with the research team on ongoing projects, exchanging ideas and insights, and contributing
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research in formal verification, machine learning and artificial intelligence system assurance. The successful candidate will develop new techniques and tools for analysing, verifying and improving
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• SLAM, autonomous driving and mobile robotics • Remote sensing, image/video processing and computer vision • Object detection, tracking and classification using deep learning • Monitoring
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and facilities in high-performance computing, machine learning and artificial intelligence, human-system interaction, network science, sensing, large-scale structural testing, materials manufacturing
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-computer interaction and design; automated self-driving laboratories that pair hardware instrumentation with active-learning machine learning for experiment design to accelerate chemistry, materials, and
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++, Java, Julia, or other competent languages. A good record of publications in reputable peer-reviewed journals in maritime transport, logistics management, machine learning, deep learning, and optimization