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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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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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Associate) PhD in smart grids, energy management, or mathematical optimizations (for Research Fellow). The candidates who have submitted the thesis are also encouraged to apply. Background in the application
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to the scientific community Job Requirements: PhD in chemistry, physics, material science, computer science or an allied field Experience with quantum computing frameworks, specifically Pennylane and Qiskit
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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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Requirements: PhD (for Research Fellow and above equivalent) degree in mathematics or related field Expert in (Numerical Methods) for McKean Vlasov PDEs and SDEs The College of Science seeks a diverse and
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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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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