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Field
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at the Institute and affiliated academic departments. What you’ll do: Designing, developing, and deploying modern AI/ML models—including deep learning, foundation models, multimodal architectures, and generative
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, Engineering, Physics, Medical Physics, Applied Math, Statistics, or a related field. * Strong background in deep learning (e.g., CNNs, transformers) and familiarity with emerging techniques for reasoning and
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or working with complex datasets, ideally from experimental or sensor-based systems. Hands-on experience with machine learning, including deep learning, probabilistic models, or physics-informed approaches
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data with good utility. This project involves development of deep learning based synthetic data generators that obtain both good utility and protection of privacy, through tailored model approximation
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experience in statistical modelling, machine learning/deep learning, genomics and multimodal biological, and biobank data analysis. Proficiency in R, Python, Perl, and Linux environments. A track record of
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have high-quality peer-reviewed conference/journal papers in deep learning, and natural language processing fields. Applicants are invited to contact Prof. Boris Ng at telephone number 2766 4021 or via
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equivalent. Independent, highly analytical, proactive and a team player Excellent teamwork and verbal, written communication skills In-depth knowledge of deep learning, specifically, foundation models
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and Applied Sciences Department/Area Electrical Engineering/Computer Engineering/Computer Science Position Description Project Deep learning plays an essential role in the operation of an autonomous
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on human behavior modeling related to video classification using deep learning networks for end-users. Work with other team members to develop and maintain software for maximum efficiency and usability
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, and the ability to read related scientific papers on cancer combination therapy. It would also require expertise in relevant AI methodology, such as deep learning architectures for property prediction