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critical role in advancing the research mission of the department by leading and supporting high-impact research projects. The position will contribute specialized technical and analytical expertise, oversee
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knowledge and hands-on experience in: Deep learning frameworks (e.g., PyTorch, TensorFlow) Deep learning models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and
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models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and algorithms Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly
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. This postdoctoral position provides computational research capacity within DCMB to execute a research program at the interface of genomics, network science, and behavioral data modeling relevant to gene expression
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information for three (3) references; Cover Letter, describing your current and future research interests, as well as your expected start date for the position. In addition to submitting your application
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of consciousness, including sleep, anesthesia, coma, and non-ordinary states induced by psychoactive compounds. Responsibilities include experimentation and data collection in human participants using
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for this position, please submit the following materials as part of your application package: Curriculum Vitae (CV), including a publication list and contact information for three (3) references; Cover Letter
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. The Fellow will work in close partnership with the lab's experimental team to build and apply analytical frameworks that translate these data into mechanistic insight and therapeutic hypotheses. As part of
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experimental team to build and apply analytical frameworks that translate these data into mechanistic insight and therapeutic hypotheses. As part of the Bioinnovations in Brain Cancer initiative, the Fellow will
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. Effective analytic skills with at least two years of demonstrated professional experience writing code to perform statistical analysis of large data sets using software such as Stata, SAS, or R. Demonstrated