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security-related projects. Disseminate research findings through conferences, invited talks, and outreach activities, strengthening NTU’s leadership in infrastructure security R&D. Job Requirements: PhD in
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the Vice Dean, Research and Dean. Person Specification Qualifications, Knowledge and Experience: MBBS/MD and/or PhD degree(s) Relevant postdoctoral or clinical experience Significant experience and
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Investigator (PI) or team lead with project management tasks. Job Requirements: PhD degree in Optimization, Artificial Intelligence, Transportation or Aerospace. Evidence of developing Machine Learning and
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and contribute to evolving methodologies. Present findings to academic and industry stakeholders. Job Requirements: PhD in Biomedical Engineering, Biochemistry, Biology, Chemical Engineering
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Professor Rank Candidates should possess: A PhD or equivalent qualification in technology-enhanced learning or learning sciences Research expertise in VR, AR, ILE or related multimodal immersive technologies
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Requirements: PhD degree in physics, engineering or related field. Familiarity with electrodynamics and electromagnetism. Good written and oral communication skills. Proficiency in scientific programming e.g., C
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Requirements: MSc (Research Associate) or PhD (Research Fellow) in Mathematics or Computer Science or closely related fields. Ability to design and implement advanced algorithms and data structures. Independent
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science, machine learning, artificial intelligence, or a related field. Candidates with a PhD may be considered for a Research Fellow position instead. Prior experience with video data visualization research will be
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Requirements: PhD degree in physics Demonstrated experience in computer sciences Demonstrated experience in handling large size database Knowledgeable in theoretical physics, and at minima basic knowledge in
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: PhD in Materials Science, Chemistry, Physics, Computer Science, or a related field. Strong expertise in machine learning for materials science (e.g., generative models, neural networks, active learning