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with archival best practices, including those related to research, processing, and/or digitization; basic familiarity with current approaches to digitization, image manipulation, file management, and/or
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with archival best practices, including those related to research, processing, and/or digitization; basic familiarity with current approaches to digitization, image manipulation, file management, and/or
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Tiny Machine Learning (TinyML). The role will focus on the design and development of battery‑less, ultra‑low‑power IoT systems capable of executing secure TinyML‑based visual perception algorithms
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with archival best practices, including those related to research, processing, and/or digitization; basic familiarity with current approaches to digitization, image manipulation, file management, and/or
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with archival best practices, including those related to research, processing, and/or digitization; basic familiarity with current approaches to digitization, image manipulation, file management, and/or
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with archival best practices, including those related to research, processing, and/or digitization; basic familiarity with current approaches to digitization, image manipulation, file management, and/or
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Experience with Python and machine learning libraries (PyTorch, TensorFlow, scikit-learn). Understanding of deep learning, CNNs, Capsule Networks, and image processing fundamentals. Basic knowledge of handling
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on the laboratory can be found here. Our group develops machine learning algorithms to automatically generate discoveries from large-scale brain imaging data. We aim to uncover fundamental principles
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. Qualifications Minimum: Master’s degree in engineering or a related discipline (e.g., Mechanical, Electrical, Computer, Energy, Materials, Mechatronics). Preferred: PhD’s degree in a relevant field. Prior
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excellent organizational skills. Background check required. Position contingent on funding. Working Conditions Office, Library, Computer Room; Requires extensive safety training Physical Effort Typically