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an initial focus on first-year writing in our team-taught Engineering 100 courses; on intermediate technical communication in TCHNCLCM-300 (our standalone course for Computer Science and Computer/Electrical
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. Provides input into the development of mathematical and/or computer models for analyzing experimental data. Trains users in equipment operation and laboratory techniques. Explains and demonstrates
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Qualifications* Master's or PhD degree (completed or expected soon) in computer science, computer engineering, or a closely related discipline Experience in machine learning and/or artificial intelligence
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. Maintain current awareness of and facility with using state-of-the-art machine learning methods in computational drug discovery, especially those that are available through opensource platforms. Evaluate
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code for image processing and analysis, including single cell imaging and tracking Knowledge of artificial intelligence and physics-based computational models for cell behavior Experience with cell
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). cloning, and protein expression, ELISA, western blotting, biochemical and cellular screening assays Bioinformatics, computer programming experience (R, Python) Evidence of qualification is provided by
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, MATLAB, R, signal processing, machine learning, and time series analyses would be particularly useful but is not a prerequisite; an eagerness to learn and expand knowledge in these areas is expected
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, such as Seurat and Scanpy. Experience with machine learning models, such as transformer and diffusion models. Strong written and oral communication skills. Modes of Work Positions that are eligible