45 mechanical-engineering "Imperial College London " Fellowship positions at Nanyang Technological University
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Requirements: Candidates should hold a Ph.D. Degree in Materials Science and Engineering, Mechanics, Biomedical Engineering or related field. Expertise in nanomaterials, MD and DFT simulations, modeling. Solid
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documentation. Job Requirements: PhD degree in Civil Engineering or in Engineering in a closely relevant field. Familiar with data and models, and software used in exposure and loss modelling. Competency in
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The School of Mechanical & Aerospace Engineering (MAE) is a robust, dynamic and multi-disciplinary international research community comprising of world-class scientists and bright students. MAE
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studies related to food digestion. Key Responsibilities: Develop solid state and submerged fermentation technology Handle planning and execution of experiment Monitor research progress Manage lab
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on the development of security and privacy enhanced solutions for AI accelerators. Job Requirements: PhD’s degree in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Strong background in
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intelligence, humanities, social sciences and the arts, finance, health, and engineering. It will also translate state-of-the-art scientific results into real world solutions for Singapore, and it will transfer
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and study materials at extreme temperatures, pressure and under controlled chemical environments Prepare literature surveys to outline research / technology development in the field Employ and develop
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basic administrative support Job Requirements: Ph.D. in Operations Research, Mathematical Optimization, Industrial Engineering, or a related field, before the anticipated start date of June 1, 2024
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, Engineering, Neuroscience, Psychology, or a closely related field. At least 3 years of postdoctoral research experience, preferably within a research institute or academic setting, with demonstrated expertise
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degree in computer science, software engineering, mathematics, or a related field. Strong background in multimodal learning, natural language processing, domain adaptation, and knowledge graph