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patients with cancer; to identify and validate predictive biomarkers of clinical outcomes in cancer; and perform meta- analyses using the Bayesian framework. The projects will lead to both collaborative and
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methodologies. Understanding of integrating Bayesian approaches in NN-based model Knowledge of model deployments to cloud platforms or past work with AutoML tools. Knowledge of MLFlow for maintaining model
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on the development of Bayesian statistical/machine learning methods for the data integration analysis of high-throughput imaging and molecular data (i.e., genome, transcriptome, epigenome, and more). The methods would
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at the intersection of systems neuroscience and computational modeling. Our lab is broadly interested in Bayesian inference, perception, multisensory integration, spatial navigation, sensorimotor loops, embodied
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(classic; Bayesian), machine learning, or other statistical approach with accompanying expertise in whatever stats package(s) is desired (SPSS; R; Stata; SAS; NumPy or PsyPy; etcetera). A strong ability to
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areas Biomedical applications, social determinants of health or other demographic health areas Spatial microsimulation, spatially weighted regression, combinatorial optimization or Bayesian network
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rate standardization, stock assessment models, and statistical modeling in both frequentist and Bayesian frameworks • A solid track record of publications Salary Information Commensuate with experience
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experience in developing models to analyze temporal data. Prior experience in utilizing Bayesian modeling in related applications. Required Application Materials: CV Contact information for reference letters
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Expertise in quantitative modeling, computational and/or Bayesian methods Expertise using at least one programming languages in the analysis of scientific data such as R, Python, Matlab, or Julia. Expertise