225 phd-neural-computation-"Multiple" Fellowship positions at Nanyang Technological University
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other duties relevant to the programme of research. Job Requirements: PhD degree or at least 8 years working experience in Computer Engineering, Computer Science, Electronics Engineering or equivalent
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infrastructure security R&D. Job Requirements: A PhD in Computer Science, Software Engineering, Artificial Intelligence, or a related discipline. Proven research track record demonstrated by publications in top
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of research. Job Requirements: Preferably PhD in Computer Science or equivalent. Independent, highly analytical, proactive and a team player Excellent teamwork and verbal, written communication skills In-depth
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Young and research-intensive, Nanyang Technological University, Singapore (NTU Singapore) is ranked among the world’s top universities. NTU’s College of Computing and Data Science (CCDS) is a
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. Perform any other duties relevant to the research programme. Job Requirements: PhD in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly analytical, proactive
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and journals. To be involved in the mentoring of PhD/Masters/FYP students. Job Requirements: Preferably PhD in Computer Science, Electrical & Electronic Engineering, or equivalent. Background knowledge
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duties as assigned by the Centre Director and Assistant Director Job Requirements: PhD in Computer Science, Cognitive Neuroscience, Life Sciences, Engineering or a related field Minimum 3 - 5 years
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by using explainable AI To develop generative AI techniques to design novel biologics for cancer Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering or
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of reports/papers and present in international peer-reviewed conferences and journals. To be involved in the mentoring of PhD/Masters/FYP students. Job Requirements: Preferably PhD degree in computer
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. Job Requirements: Preferably PhD in Computer Science or related field. Background and familiarity with the implementation and deployment of machine learning pipelines in embedded systems (e.g., robotic