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Field
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with fabrication engineers to translate physical processes into machine learning models, design and train deep learning architectures, and evaluate their ability to generalise across different process
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to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and
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, spectroscopy, and electrical performance measurements. You will work closely with fabrication engineers to translate physical processes into machine learning models, design and train deep learning architectures
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with fabrication engineers to translate physical processes into machine learning models, design and train deep learning architectures, and evaluate their ability to generalise across different process
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machine learning algorithms. • Has laboratory experience in designing, conducting, and instrumenting structures. • Strong written and spoken communications. • Open to fixed-term contract Apply now
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Job related to staff position within a Research Infrastructure? No Offer Description As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff
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focus on simulation, prototyping, lab testing and developing smart connectors for modular floating breakwaters. Key Responsibilities: Design and build connector models Simulation and optimization
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catalytic mechanism and enhanced applicability of catalyst design. By integrating machine learning with energy science, the fellow will play a key role in shaping the future of energy chemistry, furthering
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qualifications, experience, research domain(s), including professional and academic experience, and near term goals Link to a portfolio, containing relevant design work done professionally and/or academically and
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specific responsibilities in the project: i. Develop, train, and optimise deep learning models for object detection, classification, and segmentation using real-world datasets. ii. Design and implement