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learning and Bayesian methods for applications in computational biology and medical informatics. A PhD student under his supervision will lead the machine learning part of the project, developing, optimizing
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leverage your Machine Learning (ML) expertise to expand our effort in ML predictions and their translational application to rational vaccine design. You will be instrumental in leading the development
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clinicians or contingent workers assigned to the project; may supervise, mentor, and/or develop others. QUALIFICATIONS Science degree (PhD, PharmD) and minimum of 9 years Clinical Research experience in
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, machine learning, probabilistic causal models, and unsupervised algorithms Create asset-specific HCP segments, maps, and prioritizations, across multiple dimensions including care gaps, to guide channel mix
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asset- and channel-specific engagement metrics to guide MCCDH activities Develop, implement, and improve analytical tools (e.g., machine learning) to optimize channel mix/activations and content
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more than 3 years of experience, OR a PhD with 0+ years of experience in Polymer Science, Materials Science/Engineering or other related science/engineering 5+ years of experience Demonstrated leadership