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Field
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computational biology, data mining, human-computer interaction and experience, machine learning, meta-heuristics, networking and mobile computing, parallel and high-performance computing, software engineering
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review processes, analyze vast amounts of clinical trial data, and assess post-market surveillance data with greater precision. In parallel, AI is transforming drug discovery and development by identifying
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analyzing omics-based data sets [high-throughput, massively parallel genomic/proteomic/clinical.]; data management and analysis solutions that assist in storage, investigation and dissemination of large data
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screening (e.g., luciferase assays, high-content imaging, massively parallel/multiplexed assays, etc.) is desirable. Ability to interpret and discuss experiments and critically contribute to writing
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students in occupational/technical programs and university-parallel/college transfer programs. Teach up to 12 credit hours of in-person or online government classes per academic semester. Teaching
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exploring them. Basic data preprocessing, feature engineering, and model evaluation, or a strong willingness to gain hands-on experience. Eagerness to learn HPC concepts, including parallel computing
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
: Expertise in rare event simulation, deep learning, and developing computationally efficient approaches for simulation and modeling in complex systems is highly desirable Experience with parallel computing
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with Python and R programming languages. Experience with functional genomic technologies including massively parallel reporter assays. Biomedical informatics or biomedical research experience. Preferred
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massively parallel reporter assays (MPRAs) and CRISPR interference (CRISPRi), to investigate the regulatory genomic elements (enhancers and promoters) that control gene expression across various cell types
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for driving the intellectual agenda for the Initiative (and parallel research in India), working closely with Professor Kaur, faculty investigators, and program managers to identify and proactively pursue high