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resulting from T regulatory (Treg) cells by conducting genetic screens to overcome this suppression and to enhance CAR T cells for lymphoma. On the other hand, we also seek to apply the lessons learned from
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would include: Co-developing a hybrid machine learning/process-based model of anaerobic digestion processes Performing techno-economic and lifecycle analysis of microgrids build around novel biogas-fueled
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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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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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scientific discovery and healthcare. Projects will be in the area of extended drug delivery reservoirs for the eye. Responsibilities: Learn protocols and standard operating procedures. Conduct experiments
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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
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) natural language processing and large language models, (4) generative AI, (5) dynamic risk prediction modeling for high-dimensional survival data using longitudinal features, and (6) machine learning and
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traditional 2D geological maps into complex 3D structures by harnessing state-of-the-art data science and machine learning techniques at large scales. The research will use geophysical, geochemical, and