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                . The successful candidate will be a member of a highly interdisciplinary team including oncologists, biologists, engineers, and imaging scientists. The candidate will develop computational models of human disease 
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                , and natural beauty. The Department of Biomedical Informatics and Medical Education provides training, research, and service in education and informatics across the breadth of health sciences and health 
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                Position Overview School / Campus / College: School of Medicine Organization: Biomedical Informatics and Medical Education Title: Postdoctoral Scholar, Luo Lab - Biomedical and Health Informatics 
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                -era quantum computers to fault-tolerant/error-corrected quantum computing and simulation, and to achieve quantum advantages in fundamental physics. Our postdoctoral fellows (PDs) work with faculty 
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                the data repository and computer servers. Runs existing PET/MR brain image processing pipelines on the computer servers, produces the results, and communicates with the group members. Writes computer codes 
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                computer vision and machine learning approaches to integrate ground-based imagery, remote sensing data, and lidar data for high-resolution flood detection and mapping. Develop and calibrate hydraulic flood 
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                imaging program involving molecular oncologists, cancer biologists, computational biologists, and imaging scientists focused on detecting breast cancer and predicting response to therapy. The position is 
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                and William Wilcock where they will have access to in-house petascale computing facilities and cloud computing allocations. They will interact widely with all the participants in the project and have 
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                Position Summary The Cruchaga Lab at WashU Medicine is recruiting multiple Postdoctoral Research Associates. The NeuroGenomics and Informatics Center generates and analyzes Whole-Genome Sequencing 
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                about exploring and applying new statistical, computational, or machine learning techniques to astronomical data sets, and extending current methodology to be applicable in the era of big data. Looking