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to these challenges, working with high performance and distributed computing environments, working with large-scale machine learning models, and a proven research record of scholarly contributions through publications
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postdoctoral fellow interested in gaining trainingand experience in disease modelling and translational science. The successful candidatewill lead collaborative efforts among basic and clinical researchers and
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qualification is required at time of hire. Required Qualifications Completed Ph.D. in Ecosystem or Landscape Ecology (or related field such as ecosystem modeling, biogeochemistry, or climate change ecology
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the position Demonstrated expertise in organization and analysis of quantitative datasets and statistical modeling in Stata or R Excellent written, verbal, and interpersonal communication skills
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inferential statistics, model testing, and power analysis Desired Qualifications Demonstrated mentoring and teaching ability in the areas of statistical methods and use research-related tools Experience with
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-specific needs, but include: site evaluations or modeling, project coordination, interviews, data collection and/or analysis. Connections to professional practice and to emerging technology and ideas in
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validating deep learning models for the prediction of disease progression from ophthalmic data. Skills include working with image or computer vision-based toolkits, development of multimodal, multidata type
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, data visualizations, and models. Essential Functions Identify prospective participants, consent, enroll, collect biospecimens, disburse biospecimens to labs, track information, enter data into database
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and robustness of defenses and mitigations for AI systems, reverse engineering AI systems and models, and identifying new areas where security research is needed. We participate in communities
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collaborative efforts among researchers at the University of Utah and UC San Diego in developing and applying methods in predictive and causal modeling of complex biomedical and social processes and systems