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Science, Electrical/Computer Engineering, or a related field by the start date, with a strong publication record in computer vision, multimodal learning, or vision–language models. We require hands-on expertise with
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www.icord.org for more information. Additional information about Dr. Krassioukov‘s laboratory can be found at: http://icord.org/researchers/dr-andrei-krassioukov/. Work Performed: The postdoctoral fellows
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electrophysiology. We are seeking a postdoctoral researcher to lead the neuroimaging component of a longitudinal, pediatric drug trial in Neurofibromatosis type 1 (NF1). In this role, you will acquire and analyze
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structures, and time-dependent processes spanning molecular and cellular scales. We encourage the use of theory and computation as well as experiment, and welcome applicants who use machine-learning and
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Intelligence (AI) and Machine Learning (ML) methods to tackle complex biomedical challenges in nutrition and health. This is a one-year full-time benefits-eligible position that may be extended for up to four
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programs targeting neurobiological disorders. Required Certification, Licensure/Other Credentials Preferred Qualifications Research experience in using in vivo neuroimaging and machine learning techniques
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, integrating and interpreting them across modalities remains a fundamental challenge. The successful candidate will develop computational and machine-learning frameworks for multimodal neuroscience data
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the sequence of the human genome and the development of common diseases. You will work on a collaborative project that aims to develop Machine Learning and laboratory-based approaches, for decoding how the human
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improvements. Examples include optimizing the squeezing of the vacuum to minimize quantum noise, a prototype cryogenic interferometer, using machine learning for nonlinear feedback control, devising techniques
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, biomedicine, and other areas of societal importance. Coding and/or machine learning experiences are highly valued. Specific projects may involve developing multiscale simulation methods for quantum mechanical