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intelligence, including reinforcement learning, computer vision, and deep neural networks applied to robotics. Track record of peer-reviewed publications and active participation in interdisciplinary research
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with Dr. Emily Becker and the NOAA Climate Prediction Center. Key responsibilities Train neural networks and quantify uncertainty to evaluate predictability Perform explainable AI (XAI) related research
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platforms, as well as extensive networking opportunities within the University of Miami’s robust AI and digital health ecosystem. Program Objectives: Provide fundamental training by interdisciplinary faculty
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for interacting with colleagues and stakeholders. Department Specifics Develop various machine learning and data mining models including convolutional neural networks (CNNs), Transformers, large language models
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data analysis for omics studies, including genotype data, bulk and single-cell RNA sequencing data, high-dimensional phenotype data, pathway and functional analysis, network analysis, polygenic risk
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fundamental mechanisms of gene regulation, and transcriptional circuits including gene-regulatory networks, feedback regulation, and regulation of stochastic expression noise in multiple model systems ranging
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participatory research, and professional development, mentorship, network building congruent with the needs of racial/ethnic trainees focused on minority populations -all to ensure long-term success of program