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and development in the chemical conversion and valorization of hazardous waste streams, focusing on complex, reactive materials. Involves process design, optimization, and scale-up, including reaction
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the environmental performance of established and emerging processes. This role includes designing and conducting cradle-to-gate and cradle-to-grave LCAs, building life cycle inventory (LCI) datasets, and integrating
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++, or Go, and frameworks like PyTorch or TensorFlow, is highly advantageous. Experience in developing and deploying machine learning models, particularly in natural language processing (NLP) and large
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plasma process equipment. Characterize the influence of magnetic field on plasma flow. Conducts various design experiments to refine processes. Optimize vacuum coating processes, and leading R&D efforts in
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in signal representation/processing, esp for scent signals. Prior research experience and track record in signal detection, machine learning and deep learning. Prior programming experience in state
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framework on how to build up a generic framework to use learning-assisted approach to solve various optimization problems Develop mathematical modeling framework to find the optimal operation strategy Conduct
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. The successful candidate will take technical ownership of thin film deposition, materials characterization, and process innovation, contributing both strategic insight and hands-on leadership to research programs
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organizational and interpersonal communications with excellent written, oral communication and computer skills. A team player who is able to prioritize, multi-task and work collaboratively in a diverse workforce
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organizational and interpersonal communications with excellent written, oral communication and computer skills. A team player who is able to prioritize, multi-task and work collaboratively in a diverse workforce
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the structure and functions of the human brain, with particular emphasis on both normal and pathological cognitive processes. Located in the Experimental Medicine Building on NTU’s main campus, CoNiC is equipped