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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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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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++, 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
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collaborative and interdisciplinary activities. Job Requirements: Preferably PhD degree in Computer Engineering, Computer Science, Applied Mathematics or equivalent. Strong academic background in machine learning
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such as deep and statistical learning. Experience of executing research projects at the level of senior PhD student, including creating and debugging machine learning algorithem. Excellent communication
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machine learning and AI acceleration. Perform performance, power, and area (PPA) analysis of processor and accelerator designs. Publish research findings in top-tier conferences and journals and contribute
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, Physics, Computer Science, or a related discipline. Demonstrated research experience in computational chemistry, atomistic simulations, machine learning, or artificial intelligence for materials and
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(DFT) simulations and develop machine learning potentials to investigate zeolite-related systems. The role will focus on delivering research projects and promoting research excellence in this area. The
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system PhD in Chemistry/Materials Science/Physics Encourage initiating activities on MOF development, devising, and analytical process Experience in machine learning will be preferred Good oral and written