58 machine-learning-"https:"-"https:"-"https:"-"https:" uni jobs at SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
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Role Overview As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have opportunities to tackle real-world, industry-relevant
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cutting-edge computer vision technology. Working closely with the Principal Investigator, Co-PI, and interdisciplinary research team, RE will develop and implement deep learning algorithms to analyze trap
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by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Role Overview As a University of Applied Learning, SIT works closely with industry in our
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Science, or a related technical field Master's or PhD degree in Machine Learning, Computer Vision, or related areas will be advantageous Preferred Qualifications: Experience with biological/ecological
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The Singapore Institute of Technology (SIT) is Singapore’s first University of Applied Learning and the third largest university by intake in Singapore. Our mission is to maximise the potential
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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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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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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