239 parallel-and-distributed-computing-phd Fellowship research jobs at Nanyang Technological University in Singapore
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: A PhD in Physics, Computer Science, Mathematics, Machine Learning or relevant fields. Strong publication record in top conferences/journals, such as Nature Physics, Nature Communications, PRL, T-PAMI
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conferences, presenting results to stakeholders and industry experts. Job Requirements: Ph.D. in electrical engineering, computer science, or a related field, with a strong background in AI/ML, wireless
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of the designed algorithms and systems. Help with research presentation works such as high-quality paper writing. Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering
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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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cutting-edge research in computational X-ray photonics. The research fellow will assist in the development of novel theoretical numerical framework pertaining the generation of compact, high quality X-rays
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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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Associate Research Fellow (Military Transformations Programme) The S. Rajaratnam School of International Studies (RSIS), a Graduate School of Nanyang Technological University, Singapore, is a
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-making algorithm for autonomous vehicles. The work will involve sensor fusion, perception, trajectory prediction and test rig set-up, and experimental validation. Job Requirements: PhD Degree in Vehicle
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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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computational simulations in the area of density functional theory (DFT). The role will focus on understanding and predicting the properties of novel functional materials, including surfaces and interfaces