114 machine-learning Fellowship research jobs at Nanyang Technological University in Singapore
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, or other related fields. Solid Mathematical skills. Experience in implementing algorithms for machine learning and natural language processing-related applications (It would be good if the candidate can also
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-driven models to optimize solvent systems. Mentoring the training and learning of PhD and undergraduate students on sustainable biomass conversion topics Assist in report/document/further grant writing
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aspirations and development goals. Job Requirements: Essential Criteria Close to completion or hold a relevant Ph.D. with post-qualification research experience in statistical machine learning, and deep
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experience in IC design/Electronics Familiar with Analog/Power Management/RF/ADC/DAC IC design Proficiency in Cadence Proficiency in Python and Matlab Experience in implementing algorithms for machine learning
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, Julia. Understanding of one or more of the following areas would be an advantage Optimization Dynamic systems Control theory Power electronics Signal processing Machine learning and data science (various
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cost and improve operational efficiency) Carry out hands on experiments with 3D printing machines Support to develop journal and conference articles Coordinate and organise activities related
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of performance, speed, and precision. Key Responsibilities: Design and implement genAI models for embodied AI systems. Develop and optimize deep learning algorithms to enable robotic arms to perform complex tasks
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. Write the report for the project progress. Work with research assistant for the prototype. Job Requirements: PhD in Electrical and Electronic Engineering, Computer Engineering / Science, or related field
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. Experience in AI system security, adversarial robustness, and resilience of autonomous agents in emerging technologies and computer security (e.g., malware analysis, bug detection, program repair). Excellent
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processing would be an advantage. Proficiency in statistical software (e.g., R, Python, SAS, or Stata). Experience with clinical informatics approaches (e.g., cluster analysis, machine learning, Bayesian