184 parallel-processing-bioinformatics "https:" Fellowship positions at Nanyang Technological University
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engineering, composite science, environmental science, and so on. Key job purpose is to develop green and scalable pre-treatment process for polymer before mechanical or chemical upcycling, particularly
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fundamental understanding and practical applications of quantum correlations and information processing. We invite applications for a research position in quantum information science. The successful candidate
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processing of required PLC waveforms and perform rigorous EMC/EMI tests for compliance. Prepare technical reports and present progress to lab stakeholders and industry partners. Job Requirements: PhD degree in
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related processes for energy-efficient separations in areas such as energy, environment, and pharmaceuticals. This position offers the opportunity to contribute to high-impact projects and to work within a
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(specifically PCECs). Proven experience in developing and validating numerical models (e.g., using COMSOL). Hands-on experience with programming for numerical optimization, machine learning, and data processing
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Knowledge of electrodialysis, electrochemical systems, or ion transport processes Familiarity with surface functionalization techniques or polymer chemistry Experience in experimental design and materials
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engineering or related field At least 1 year of relevant experience in signal processing and machine learning. Good written and oral communication skills Proficiency in ANSYS, and lab test skill Ability to work
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: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering, or equivalent. Independent, highly analytical, proactive, and a team player; strong verbal and written communication skills
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research reports) and academic publication processes will be preferred. Knowledge Good knowledge of cognitive functions and cognitive neuroscience theories and techniques. Good or working knowledge
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: PhD degree in Computer Science, Electrical Engineering, or a closely related field Strong research background in computer vision and deep learning Solid experience with multimodal learning, segmentation