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-doctoral Associate will develop algorithms and theory for machine learning methods, as well as implement and apply ML methods to problems in domains such as computational biology and neuroscience. This is a
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across time and contexts. Job Description: You will develop and apply mathematical models and machine learning algorithms to analyze the structure and evolution of knowledge systems across different
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individual differences in cognitive and brain aging. The successful applicant will join an active laboratory, under the direction of Professor Max Elliott, that is addressing a wide array of questions in aging
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outcomes. The individual will be expected to develop stimulation strategies and testing algorithms, write code, and develop software. They will do extensive validation and testing, under the supervision
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sets in the lab. The focus of these manuscripts will be on different research topics of interest related to health disparities/equity, internalized stigma, international adoption, and/or cultural/ethnic
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validate signal processing algorithms and stimulation strategies using electrophysiological and behavioral data. Develop GUIs, psychophysical test protocols, and objective outcome measures (e.g., ECAP, ABR
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• Experience in flow cytometry Job Description: This position seeks a highly motivated postdoctoral fellow to investigate how cell death sensors regulate immune responses, with an emerging interest in the retina
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publications (20%). He/she will also develop and maintain different susceptible and resistant insect colonies (15%) and help lab management and mentor graduate and undergraduate students (5%). Qualifications
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outcomes ●casual representation learning for real-world data ● deep learning interpretation, fairness and robustness ●Regularly conduct computational experiments to execute algorithms on various health and