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cutting-edge advances in electrophysiology and machine learning, this project aims to create a functional "digital twin" — a model that captures both the activity dynamics of the brain at cellular
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research programs. Required Qualifications: o Highly motivated postdoctoral researcher with: • Experience in relational databases, big data curation and analysis • Expertise in machine learning, including
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language processing Machine learning applications in healthcare Clinical trial methodology Knowledge of substance use interventions Experience with large language models Grant writing experience Required Application
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the use of R and/or Python Basic understanding of statistical modeling, and machine learning Understanding of high-throughput sequencing techniques including whole genome, whole exome, targeted capture, RNA
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knowledge in bioinformatics, machine learning, statistics and programming skills (R, Python, or MATLAB) are required. The ideal candidate should demonstrate a record of publications in the area. Knowledge in
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expression in single cells within their spatial context in pathomic samples. This research opportunity will be focused primarily on the development and application new image processing and machine learning
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the development of clinical deep learning and other machine learning models to enable improved diagnosis, prognostication, and prediction of treatment response for bladder cancer, specifically related to endoscopic