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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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reclamation pilot-scale and lab-scale systems. Conduct membrane and separation process modelling, module-scale desalination system modelling, including conventional modelling and machine learning-based
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on the developed models for agencies/commercial partners Supervise junior researchers and master students Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering or
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, building automation and control (BAC) system, Artificial Intelligence (AI) & Machine-Learning (ML) applications. Good written and oral communication skills Proficiency in power system modelling, advanced
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science development Design, develop, and validate machine learning models for skill inference, mastery estimation, and personalized feedback, including Bayesian approaches, sequence models (for example LSTM
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collaborators. Mentor junior colleagues and students. Write, present, and publish research findings in peer-reviewed journals. Knowledge and Experience Requirements: PhD degree in statistics, computer sciences
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of lab-scale and pilot-scale experiments/operations with element of machine learning, sample characterizations, data analysis, and so on. Job Requirements: PhD degree in Materials Science, Polymer Science
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of scalable machine learning systems using convex/nonconvex optimization and federated learning methods. Develop software prototypes for AI-for-Science systems tailored to scientific discovery and data
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hands-on experience in areas such as computer vision, deep learning, multi-modal sensing, robotics, structural health monitoring, or digital twin technologies. (Fresh PhD graduates are welcome
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combinations of structural and functional properties, using both simulations for machine learning and experimental validation. Fabrication tools and methods are already established in our laboratory. Key