199 parallel-computing-numerical-methods-"Prof" Fellowship positions at Nanyang Technological University
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Responsibilities: Develop new methods for power systems control. Develop data-driven methods for application in power & energy systems Assist in preparation of teaching materials for electric power related courses
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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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project objectives and publishing high-quality journal articles. Key Responsibilities: Conduct experimental test, numerical simulation and life-cycle assessment on solid waste reuse and management systems
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Responsibilities: Development of stochastic and analytical methods for nonlinear partial differential equations Implementation of relevant numerical experiments using deep learning algorithms Job Requirements: PhD
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Processing and Machine Learning to develop signal processing and machine learning algorithms and methods for communication networks. Key Responsibilities: Develop signal processing and machine learning
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The role is part of NTU's Experimental Asset Markets group in the Economics Programme at the School of Social Sciences. The team focuses on lab experiments simulating asset markets to study pricing
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computer simulations Test the methods via lab experiments Publish results in top-tier journals in related fields Assist in mentoring PhD students Job Requirements: A PhD degree in control systems engineering
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, technology, engineering, and mathematics (STEM). This Fellowship provides an opportunity for early career researchers from Singapore and around the world to pursue an independent research path. This programme
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half of them published in prestigious journals (Chem. Eng., Appl. Energy, Energy Convers. Manag., Renew. Sustain. Energy Rev.). His contributions have earned his numerous fellowships and awards
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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