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research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT. The Research Engineer (Modelling and
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to the UrEco 2030+ project: “Optimizing Urban Ecosystem Services Model for Urban Climate and Biodiversity in Singapore towards 2030 and Beyond.” This position requires experience in Geographic Information
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of the CFI Project (https://www.pub.gov.sg/-/media/PUB/Resources/Press-Releases/2024/06/Annex-A_Tranche-2-Research-Projects-Awarded-Under-CFI-Singapore.pdf ). The primary role involves developing and
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, and research team to ensure timely achievement of project deliverables. Undertake the following specific responsibilities in the project: i. Develop, train, and optimise deep learning models for object
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data, inspection results and carbon footprint information. Develop, train and validate AI models to automate QA/QC processes, enabling real-time verification, anomaly detection and consistency checks
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acquisition, instrumentation and monitoring during laboratory testing Perform numerical modelling and simulation, such as finite element modelling, parametric studies, and calibration against test data. Analyse
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-behavior analysis models for detecting personnel, posture, casualties, and hazardous situations, including operation in low-visibility scenarios. Implement scene mapping, environment perception, and path
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publication record. Outstanding data analytics, mathematical, and computer modelling skills. Excellent interpersonal communication and oral presentation skills in English Self-driven and strong team spirit Open
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, or embedded software tools. Integrate controller logic with the microgrid model and validate performance under different simulated conditions. Conduct testing of control strategies under various operating
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intelligence algorithms using Python. Knowledge of machine learning or reinforcement learning techniques is highly advantageous. Experience with theoretical wireless network modelling, particularly stochastic