84 algorithm-development Fellowship positions at SINGAPORE INSTITUTE OF TECHNOLOGY (SIT) in mechanical-engineering
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to industry demands while working on research projects in SIT. The primary responsibility of this role is to contribute to an industry innovation research project on developing artificial intelligence (AI
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/ Engineer (Insect Protein Chemistry & Functional Beverage Development) As a University of Applied Learning, the Singapore Institute of Technology (SIT) works closely with industry partners to deliver
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of this role is to contribute to an industry innovation research project on developing artificial intelligence (AI) solutions for train operations and maintenance. Key Responsibilities Work closely with faculty
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Schemes of Service: Research Division: Food, Chemical and Biotechnology Employment Type: Fixed Term Research Fellow / Engineer (Insect Protein Chemistry & Functional Beverage Development) As a
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of novel edge-assisted computation offloading strategies that leverages edge intelligence. The role will bridge rigorous theoretical work with hands-on offloading algorithm design and development. The core
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will bridge rigorous theoretical work with hands-on offloading algorithm design and development. The core responsibility is to build and validate these offloading strategies, complete with Python
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learning algorithms. Collaborating with industry partners to understand operational requirements and developing AI pipelines for analysing visual and sensor data collected during inspection processes
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of Singapore’s maritime sector. The project focuses on developing planning methods to support the electrification of harbour craft fleets, using real-world operational data to derive charging demand profiles and
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-driven video understanding of consumer facial care behaviours. Work with PI and company to develop the AI solution Develop novel algorithms for: Fine-grained video understanding Concept learning Temporal
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project aims to develop next-generation AI technologies for concept-driven video understanding from consumer-recorded facial care videos. Unlike conventional action recognition, the research focuses