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Field
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sampling-based and reinforcement learning-based motion planning algorithms for multiple robotic arms in automotive manufacturing, including testing, performance evaluation in both simulation and actual
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learning algorithms. We combine statistical methods with online reinforcement learning algorithms to develop reinforcement learning algorithms and inferential tools. The successful applicant will be expected
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Professor Kalina Yacef. These projects explore how multimodal data from online platforms and sensor-based environments can be harnessed to enhance learning at scale. Research directions include designing
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transformative research initiative aimed at developing next-generation chemo-sensing platforms. The successful candidate will contribute to the development of solid-state, surface-based electrochemical sensors
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Punggol campus. Under this job title, you will be required to establish a test bed in SIT’s Punggol campus building, benchmarking of current building energy performances, deployment of new sensors
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to research-based activities, including the development of new data analysis algorithms, processing and analysis of field data, and participation in the fieldwork. Your responsibilities will include: Conduct
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work with hands-on offloading algorithm design and development for IoT networks. The core responsibility is to build and validate edge-assisted offloading strategies, complete with software APIs, through
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inequalities, the use of psychometrics and patient-centered outcomes research. The fellow will work with investigators from several institutions such as University of Maryland, Duke University, Harvard
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(CPS) for aquaculture, to sensors and platforms for maritime applications. The Postdoc’s primary task will be to lead the development of algorithm, software, and hardware to extend the current HAUCS
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edge-assisted offloading strategies for IoT networks. The role will bridge rigorous theoretical work with hands-on offloading algorithm design and development for IoT networks. The core responsibility is