23 engineering-in-image-processing Fellowship positions at SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
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with 5+ years experience OR Master's degree with 2+ years experience in Biomedical Engineering, Mechanical Engineering, Electrical / Electronic Engineering, Computer Engineering, HealthTech, MedTech
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or application. Strong technical expertise in one or more of the following areas: Computer Vision and Image Processing Machine Learning, Deep Learning, and Reinforcement Learning Large Language Models (LLMs) and
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functional brain imaging and 3D human kinematics. This funded research brings together multidisciplinary expertise from Singapore Institute of Technology (SIT), National University of Singapore (NUS), National
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areas: Computer Vision and Image Processing Machine Learning, Deep Learning, and Reinforcement Learning Large Language Models (LLMs) and Multimodal Models Generative AI, Agentic AI, Physical AI, and
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10 Jul 2026 Job Information Organisation/Company SINGAPORE INSTITUTE OF TECHNOLOGY (SIT) Research Field Medical sciences Researcher Profile Recognised Researcher (R2) First Stage Researcher (R1
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specifications, performance frameworks, and implementation methodologies suitable for industry adoption. Lead the scale-up of laboratory processes (e.g. aqueous carbonation) to prototype and pilot systems
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. Job Requirements: Competence in working with languages such as C along with Python-based coding and AI development. Have a degree in Computer Science/Computer Engineering. Possessing a Master’s or PhD
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Schemes of Service: Research Division: Food, Chemical and Biotechnology Employment Type: Fixed Term As a University of Applied Learning, the Singapore Institute of Technology (SIT) works closely
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that are relevant to industry demands while working on research projects in SIT. The primary responsibility of this role is to support and contribute to an industry innovation research project. The Research Engineer
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detection of microbial bioburden in biopharmaceutical cleaning validation processes. The role involves aptamer discovery, nanomaterial-based SERS sensor development, and integration with microfluidic systems