102 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" positions at SINGAPORE INSTITUTE OF TECHNOLOGY (SIT) in United Kingdom
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As a University of Applied Learning, SIT collaborates closely with industry partners in our research pursuits. Our research staff can gain practical research skills that are directly relevant to
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libraries such as HuggingFace Transformers, PyTorch/TensorFlow, and scikit-learn. Prior experience or coursework in natural language processing, machine learning, or information retrieval. Familiarity with
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As a University of Applied Learning, SIT works closely with industry in our research pursuits. Future ship and system design (FSSD) program develop strategic and innovative design capabilities
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, microbial cultures, and cleaning validation samples. Develop data analysis pipelines for Raman spectral classification, potentially integrating machine learning methods. Research & Project Responsibilities
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foundational knowledge in signal processing and machine learning. Working knowledge of computer vision and deep learning concepts, including object detection and image-based classification, with hands
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conditions. The researcher will also work with team members within the consortium in generating necessary data required for developing a machine learning model for storm surge prediction. Key Responsibilities
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Engineering, Computer Science, Data Science, Statistics, or equivalent. Strong theoretical background in statistics and machine learning. Knowledge of the basics of federated learning and causal inference is
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As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skills
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, accumulated deformations, and their impact on structural performance, particularly for compression members. Develop data-driven reusability assessment platforms integrating NDT data, machine learning models
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: Architect and deploy machine learning and computer vision models directly onto onboard edge devices (e.g., NVIDIA Jetson) for real-time object detection, tracking, and autonomous decision-making. Proof