122 parallel-processing-bioinformatics Fellowship positions at Nanyang Technological University
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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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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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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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. 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
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communication, or signal processing. Proficiency in programming languages like Python, MATLAB, or C++, and experience with AI/ML frameworks like TensorFlow, PyTorch, or scikit-learn. A proven track record of
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against existing commercial technologies. Key Responsibilities: Perform literature review on data available for commercial processes Provide estimations for data not available in literature Develop process