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Field
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of pioneering the next generation of human-induced pluripotent stem cell (iPSC)-derived organoid models. We focus on creating organoids with precise cell types, architecture, and functions that closely mirror
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B lymphocytes. Specifically, we investigate how transcription, chromatin architecture, DNA replication and epigenetic features regulate the key processes of antibody somatic hypermutation and class
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-doctoral researcher position. Led by Prof. Nektarios Tsoutsos, the Trustworthy Computing Group specializes in the broader areas of cybersecurity, applied cryptography, computer architecture, hardware
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dissertation close to submission in architecture (architectural history), art history, history, urban planning, and related subjects excellent knowledge of English other relevant language skills for researching
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, and use of novel architectural features. Argonne National Laboratory is a multi-disciplinary research institution offering world-class opportunities in High-Performance Computing and housing the Argonne
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of the post-doc is to study how innovations in AI, especially adaptation of Large Language Models (LLMs) architectures for time-series data, can be used in study of aging, health span, and longevity
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approaches for important questions in neuroscience. We have multiple current and incoming NIH projects to establish cellular cell type architecture maps of mammalian brains using mice as an animal model. Three
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architectures, primarily Intel FPGAs. The successful candidate will collaborate closely with researchers at McGill and partners worldwide, leveraging industry-standard tools and technologies to ensure
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postdoctoral Researcher who is willing to embrace new approaches for important questions in neuroscience. We have multiple current and incoming NIH projects to establish cellular cell type architecture maps
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for modern heterogeneous architectures, including CPUs, GPUs, and other accelerators, the project seeks to achieve unprecedented efficiency and resolution in plasma simulations. This advancement will enable