361 computer-programmer-"St"-"Washington-University-in-St"-"St" Fellowship positions in Singapore
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for Quantum Technologies (CQT) in Singapore brings together physicists, computer scientists and engineers to do basic research on quantum physics and to build devices based on quantum phenomena. Experts in
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highly numerate subject (e.g. engineering, mathematics, physics, chemistry, statistics, econometrics, computer science, climate science) is advantageous. For Research Fellow and Senior Research Fellow
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for Quantum Technologies (CQT) in Singapore brings together physicists, computer scientists and engineers to do basic research on quantum physics and to build devices based on quantum phenomena. Experts in
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The School of Materials Science and Engineering (MSE) provides a vibrant and nurturing environment for staff and students to carry out inter-disciplinary research in key areas such as Computational
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Medical School. In August 2024, we welcomed our first intake of the NTU MBBS programme, that has been recently enhanced to include themes like precision medicine and Artificial Intelligence (AI) in
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. Write the report for the project progress. Work with research assistant for the prototype. Job Requirements: PhD in Electrical and Electronic Engineering, Computer Engineering / Science, or related field
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. robotics, computer science, electrical/electronic engineering, etc. • Extensive experience in robotic motion planning and control, especially in obstacle avoidance and contact-aware task execution with
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expected, and familiarity with modern machine learning methods will be considered an asset. NUS offers a vibrant research environment, with access to high-performance computing facilities and opportunities
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career in academia or in the quantum industry. The open position is extremely well funded by Singapore’s ambitious Quantum Engineering Programme and Competitive Research Programme. The PI hold the
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems