16 phd-in-power-electronics Fellowship research jobs at Nanyang Technological University in Singapore
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Requirements: PhD in Mechanical, Electrical and Electronic Engineering or related field 4-year research experience in related area Expertise in large animal studies, such as swine and sheep Knowledge in
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. Contribute to the preparation of research proposals and project reports. Job Requirements: PhD in Mechanical and Automation Engineering, Electrical and Electronic Engineering, or a closely related field
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Requirement: PhD in Electrical and Electronic Engineering or related field 1-year research experience in related area Expertise in Power Electronics Strong publication track record is preferred Hardware
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research. Job Requirements: A PhD in Computer Science/Engineering, AI or related areas. Strong background in machine learning and computer vision. Prior experience in multi-modality learning, multimedia
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, focusing on loss and thermal analysis. Experimental testing for high-power motor system, focusing on loss and thermal analysis. Job requirements: PhD in Electrical and Electronic Engineering or related field
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for converting intermediates derived from biomass into precursors for biofuels. The research fellow will employ electronic structure calculations, first principles thermodynamics, and microkinetic modelling
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: A PhD in Computer Science or relevant fields; Strong background in machine learning, deep learning and computer vision. Prior experience in 3D vision is preferable. Strong publication records in top
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satisfactory performance. Successful candidates will be involved in a project that is related to generative design. Key Responsibilities: To independently undertake research in computer vision and deep learning
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the project. To attend, contribute, and where necessary lead relevant meetings. To undertake any other duties relevant to the programme of research. Job Requirements: A PhD in Computer Science or relevant
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intelligence techniques for the analysis of heterogeneous health data. This includes applying machine learning and computer vision algorithms to interpret complex datasets, with the aim of improving diagnostics