127 parallel-processing-bioinformatics Fellowship positions at Nanyang Technological University in Singapore
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Responsibilities: Electrochemical process on interface phenomena Battery testing under different conditions Simulation of scaled up process. Interface with machine learning group on data base set up Battery safety
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forward the use of phase field models in earthquake rupture dynamics and fluid-driven fracture processes. The project bridges applied geophysics and computational mechanics, and is jointly developed with
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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
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and approaches to improve the software engineering process in Continental, especially requirement engineering and testing Conducting the research in combining AI techniques with formal methods
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simulate orbital scenarios for sensor calibration and data fusion. Model complex orbital dynamics for accurate sensor calibration. Develop AI models for onboard and ground-based satellite data processing
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, lamination, and testing. He/she will contribute to the development of new application driven materials and production processes, located mostly at Nanyang Technological University. Key Responsibilities: Lead
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of deliverables. Lead the design and execution of surface modification processes for fiber materials, including chemical grafting, functional coating, and plasma or other physical treatments. Develop and optimize
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/ machine learning algorithms to support research in the IDMxS Analytics Cluster. The RF will apply/ improve machine learning algorithms to process (e.g., classify, predict) data collected by IDMxS. Help
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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
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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