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related field with emphasis on biosignal analysis and imaging systems. Proven experience in visual behavior analysis, video processing, or physiological signal interpretation, preferably in aquatic
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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researcher in natural language processing and large language models to work with a team from multiple disciplines of machine learning and artificial intelligence to develop multimodal large language models
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strategies, SiP and chiplet architecture Fault tolerance, robustness and reliability Functional / Cryptographic agility in hardware Prototype and design new architectural innovations, including chiplet
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chiplet architecture Fault tolerance, robustness and reliability Functional / Cryptographic agility in hardware Prototype and design new architectural innovations, including chiplet oriented architectures
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with ultrastructural analysis. Manage, process, and analyze large imaging datasets generated from the project. Assist in mentoring and training PhD and undergraduate students in relevant techniques and
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early intervention alerts in aquatic environments. Integrate open-source and commercial aquatech tools (e.g., air and underwater imaging, biosensor platforms) to build proof-of-concept detection pipelines
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neuroimaging experiments, proficient in image processing and programming paradigms. The successful candidate will contribute to ongoing multidisciplinary research and play an active role in developing novel
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programming languages such as C and Python Proficiency in deep learning frameworks such as Pytorch and Tensorflow Knowledge in imaging and computing device and equipment Good written and oral