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Field
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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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to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and
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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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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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skills, including statistical analysis using R, Python, SPSS, Stata, or equivalent software. • Experience with computational social science methods, including NLP, machine learning, LLMs, social media
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designing and building visualization dashboards. Research experience in human-centered AI, or in the integration of AI and machine learning methods into interactive visualization and analysis systems. Strong
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computational chemistry, reaction network analysis, and machine learning for organometallic catalytic reactions. 2. Design of membrane-permeable macrocyclic peptide drugs via machine learning structure
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: To independently undertake research in machine learning. To produce research reports and/or publications as required by the funding body or for dissemination to the wider academic community. To provide guidance and
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Responsibilities: To independently undertake research in machine learning. To produce research reports and/or publications as required by the funding body or for dissemination to the wider academic community
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framework to find the optimal operation strategy Conduct computer programming to verify the efficiency of the designed solution algorithms Analyze data acquired from the field survey Develop machine learning