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Field
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shoulders up to 25 pounds. Standing and sitting at computer terminals and laboratory bench completing repetitive tasks for extensive periods of time. Continuous working with materials requiring personal
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of machine learning models for object detection, classification, and outlier detection across multiple sensor modalities. Depending on previous experience, provide scientific oversight to volunteers, students
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score derivation and validation, and other relevant analyses. Develops R or Python scripts for data analysis, statistical modeling, and machine learning techniques, ensuring reproducibility and efficiency
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standardize large-scale multi-omics datasets and build databases Perform integrative and exploratory analyses of multi-omics datasets and apply machine learning methods to uncover underlying biological
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science, or in a related field Required Related Experience Computer programming, statistical modeling Required License/Registration/Certification None Physical Requirements Sitting for extended periods of time
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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experience in amino and fatty acid requirements and supplies. Effective use of statistics and modeling tools in support of decision-making tools. Proven ability to work independently and learn continuously
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account options for medical and childcare expenses Robust free training access through LinkedIn Learning plus professional conference opportunities Tuition assistance Expansive employee discount program
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Responsibilities 1. Research and Innovation:Conduct cutting-edge research in AI, machine learning, and multimodal learning, with a focus on developing Large Language Models (LLMs) that process and generate
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Spectroscopy (dwSCOS) sensor for non-invasive microvascular monitoring. 2. Perform Monte Carlo simulations for optical modeling of photon propagation in layered tissues. 3. Fabricate and characterize dynamic