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Field
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transmission and spread. Learning Objectives: Under the guidance of a mentor, the participant will learn to or increase their ability to: collect and extract samples for sequencing. construct libraries and
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Engineering, Industrial Engineering, Machine Learning, Artificial Intelligence, or related fields Expertise in numerical simulation of multi-physics systems, especially fluidic problems Expertise in generative
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record of computer programming. Experience in numerical relativity and/or numerical simulation of quantum field-detector interaction Strong written and oral communication skills. Demonstrated ability
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will be adapted to the candidate’s background and the evolving needs of the center. Possible directions include the application of rock physics models, Bayesian inversion methods, and machine learning
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use of molecular assays such as real-time PCR and next-generation sequencing to study the prevalence of infectious diseases in the target study populations. Learning about sample testing under a
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developing and implementing machine learning/AI solutions using relevant languages and frameworks Excellent communication skills and proven ability to collaborate with diverse stakeholders Technology and
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in writing grant applications and working with machine learning approaches such as MaxEnt, random forest, neural network. Experience using Geographical Information Systems and ecological niche modeling
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expertise in machine learning and/or Bayesian models is preferred. This position will involve both methodology development and analysis of multi-omic sequencing data, including spatial transcriptomic data
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aspirations and development goals. Job Requirements: Essential Criteria Close to completion or hold a relevant Ph.D. with post-qualification research experience in statistical machine learning, and deep
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SUMOylation, transcription factors, or chromatin dynamics. Expertise in machine learning or statistical modeling for biological data. Knowledge of enhancer-promoter interactions and 3D genome organization. All