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that connect animal health, data analysis, and human behavior. Design and carry out field studies, collecting and analyzing large data sets. Use and evaluate AI tools to support research and training. Create
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satisfactory performance and funding availability. Applicants should possess a Ph.D. degree in epidemiology, biostatistics, applied mathematics, data science or other related disciplines. They should have strong
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will work on multiple projects funded by NIH/NHGRI. The objective of the position is to develop novel statistical methods and computer software and analyze large scale biological data from biobanks
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expertise in Neuropixels or other large-scale in vivo electrophysiology techniques. An expert neural data analyst may be considered even with minimal animal experience. Required Application Materials: Cover
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with a large and diverse research team. Previous experience with bioorthogonal chemistry and purification and characterisation of modified biopolymers by advanced mass spectrometry is desirable. We
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available within the Department of Medicine, Division of Endocrinology in the Laboratory of Brain Behavior and Big Data at the University of Southern California. The applicant with work with PI and
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rules which enable effective learning in large and deep networks and is consistent with biological data on learning in the cortex. In particular, the research will focus on evaluating and extending a
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robust models – and for clinicians, whose goal is to determine when to trust the models. We therefore seek candidates who have strong technical background in working with large-scale deep learning models
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analyzing publicly available data on employment, education and higher education residing in large federal data sets (e.g., Census data, NCES data). Conducts research and prepares project-related technical
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(Python, MATLAB) for processing and analyzing large or multimodal datasets (e.g., neural activity and behavior). Experience working with quantitative behavioral or electrophysiological data collection