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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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equipment components Procurement of experimental consumables, prototype manufacturing and assembly of wave energy equipment Liaison and organization of large-scale indoor tests of wave energy equipment
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, figures, and presentations Key Competencies and Requirements: PhD in biology, medicine, or related field > 2 years of postdoctoral experience in cancer biology or metabolism Expertise in live cell imaging
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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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. The roles of this position include: Development of algorithms to improve memory/sample/time efficiency of LLM training. Development of a workable prototype system with capabilities such as conversational
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cortical neurons, functional loss-of-function approaches, single-nucleus RNA sequencing (snRNA-seq), xenotransplantation, and advanced in vivo imaging (e.g., 2-photon calcium imaging), the aim is to uncover
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cluster provides shared access to state-of-the-art instrumentation across multiple imaging platforms and techniques, technical expertise, and assistance in optimal image collection and analysis
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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