216 phd-computational-mechanics-"FEMTO-ST"-"FEMTO-ST" Fellowship positions at Nanyang Technological University
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to the project. Job Requirements: PhD degree in Computer Science, Electrical and Electronic Engineering, or related field. Min 3 years of relevant experience in computer vision, artificial intelligence, etc
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involve algorithm development, simulation, test rig set-up, and experimental validation. Job Requirements: The candidate must at least have a PhD Degree in Mechanical Engineering, Automation, Control
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research papers for publication Assist the supervision of final year undergraduate student’s projects Job Requirements: PhD in Computational Mathematics Strong knowledge of advanced computation and analysis
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) who will develop computational electromagnetics codes for rapid characterization of the fields scattered from artificial metasurfaces. Key Responsibilities: The key responsibilities of the Research
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Requirements: PhD degree in Computer Science, Mechatronics, Robotics, Electrical Engineering or equivalent. Proficiency in programming, software design and development and algorithms. Strong analytical
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to the applications of mathematics in cryptography, computing, business, and finance. PAP covers many areas of fundamental and applied physics, including quantum information, condensed matter physics, biophysics, and
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on incentive mechanism design. Help supervise PhD, master and undergraduate students, and R&D staff. Help with federated learning technical platform design and development. Help with collaboration with existing
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morphing mechanism based on pneumatic or electric actuation. Perform testing and validate the performance of the developed robotic system. Job Requirements: PhD Degree in Mechanical Engineering, or related
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(PhD) (completed or near completion) in Human-Computer Interaction (HCI), Communication, Computer Science, Information Science, Psychology, or a related field. Proven track record with publications in
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superlattices (twistronics). The role will focus on developing and applying theoretical models and computational quantum chemistry and machine learning methods to uncover novel properties and phenomena in low