226 phd-computational-"IMPRS-ML"-"IMPRS-ML" Fellowship positions at Nanyang Technological University in Singapore
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junior students/researchers Job Requirements: PhD degree from a reputable university in chemical engineering, environmental engineering, mechanical engineering, etc. Excellent journal paper publication
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junior students/researchers Job Requirements: PhD degree from a reputable university in chemical engineering, environmental engineering, mechanical engineering, etc. Excellent journal paper publication
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/researchers Job Requirements: PhD degree from a reputable university in chemical engineering, environmental engineering, mechanical engineering, etc. Excellent journal paper publication record. Proficient oral
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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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the stability of power-electronic-based power grids. Job Requirements: PhD degree in electrical engineering or related field Strong knowledge of power electronic converters, converter control, and stability
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beyond Conduct world-leading research in the field of Global Asia Job Requirements: PhD in a relevant discipline Conduct world class research in the history and politics of Global Asia Possess more than 10
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analysis. Prior experience in sustainability standards and green building accreditations would be advantageous. Job Requirements: PhD degree in Engineering (e.g. Mechanical, Clean/Renewable Energy or
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. Perform thermal characterization and real-application tests. Prepare monthly reports and presentations for meetings and project deliverables. Mentoring of PhD students. Job Requirements: Possess PhD degree
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Fellowship Programme) and with external partners. Job Requirements: Candidates must have a PhD in one of the relevant fields of Asian Studies, international political economy, political science, public policy
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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