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patients and established pipelines for data pre-processing, analysis, and integration (Good et al. Nat Med 2022; In Preparation). We now seek to build generative artificial intelligence (AI) models
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survival data using longitudinal features, and (6) machine learning and deep learning for analyzing time-to-event outcomes, or (7) radiomics and medical imaging analysis. Required Qualifications: We seek
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model APIs, cloud computing environments, and R for additional statistical analysis. For decision support prototype development and evaluation, web-based user interface design, human-computer interaction
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(community interventions, community-based participatory research, meta-analysis and bias in research, RCT methods, causal interference, mathematical modeling, and econometrics) Policy research related
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(broadly defined, from school to the workplace). Demonstrates excellent written and verbal communication skills and use of rigorous, innovative quantitative and/or qualitative methods of analysis. Is
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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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microenvironment architecture that drives cancer progression and therapeutic resistance. The successful candidates will perform spatial omics profiling and analysis to infer biological and clinical insights and
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care providers in identifying and managing patients with referral-warranted oculoplastic disease. Required Qualifications: - MD or PhD with experience in cell culture, data gathering, data analysis
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experience with analysis of large health databases, such as claims or electronic health record data. Required Application Materials: Curriculum vitae Cover letter describing relevant experiences, interests
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bioinformatics tools for data analysis Required Application Materials: Curriculum Vitae Stanford is an equal opportunity employer and all qualified applicants will receive consideration without regard to race