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-informed machine learning. The ideal candidate will have a strong background in developing and integrating probabilistic graphical models, Bayesian networks, causal inference, Markov random fields, hidden
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equilibrium/simulation, surrogate models/ reduced order emulators or Bayesian or interpretable machine learning. Simulation and optimization of on-demand transportation services or novel transit systems and
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datasets. Proficiency with geometric morphometrics and image alignment. Proficiency in applying quantitative genetic methods to large datasets. Proficiency with large-scale animal models using Bayesian
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significant external research funding. Experience supervising doctoral or postdoctoral researchers. Expertise in Bayesian and/or adaptive trial designs and dose-finding methodologies. Strong leadership and team
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. Demonstrated experience designing analytical frameworks, and experience using machine learning algorithms and Bayesian statistics within the R-language. Demonstrated experience managing project workflows and
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, multidisciplinary, and international body of participants including hundreds of students, faculty, and practitioners. More information about the General Sessions is available here: https://myumi.ch/EkJbp As a perk of
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· Demonstrable problem-solving skills · Ability to propose and apply novel (literature based) and innovative ideas for solving a problem Desirable · Knowledge of Bayesian uncertainty techniques
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including vacation and sick leave Comprehensive insurance options including health, dental, vision, and life insurance Learn more about working at UNL: https://go.unl.edu/aboutus As an EO employer
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– a public-private partnership conducting phase II trials of new regimens for the treatment of tuberculosis (https://www.unite4tb.org/). Application of Bayesian methods for evidence synthesis
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, candidates with research interests in areas such as Bayesian methods, survival analysis, experimental design, functional data analysis, clinical trials, precision medicine, and meta-analysis are especially