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. Establish and validate new protocols, optimizing them for the lab's specific research questions and infrastructure. Document new methods thoroughly and train lab personnel on their implementation. Team
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TensorFlow. The successful candidate will be able to develop, implement, test, troubleshoot, and optimize AI/ML solutions; automate analytical workflows; investigate complex computational or system issues
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tissues, cell culture, organoid systems, genetically modified mouse models, and other translational disease models. Develop, optimize, and standardize experimental protocols and workflows. Ensure rigor
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and/or Windows environments Working knowledge of modern development tools (Git, CI/CD pipelines, automated testing frameworks) Knowledge of data structures, algorithms, and performance optimization
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performance optimization Experience with cloud platforms, distributed systems, or container technologies (Docker, Kubernetes) Ability to produce clear, maintainable, and well-documented code Systematic
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performance optimization Experience with cloud platforms, distributed systems, or container technologies (Docker, Kubernetes) Ability to produce clear, maintainable, and well-documented code Systematic
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also involve the development and implementation of targeted and higher-throughput drug-discovery screening approaches, including assay optimization, compound testing, quantitative imaging, and validation
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optimization algorithms for complex trial design spaces. Required qualifications: A PhD or equivalent doctoral degree in biostatistics, statistics, applied mathematics, operations research, computer science
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for predictive modeling, high-dimensional optimization, uncertainty quantification, forward and inverse design, including the development of Digital Twin components, under the supervision of Prof. Douglas H
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the engineering PhD program. Faculty also will be expected to participate in recruitment and retention activities, advise both undergraduate and graduate students, and supervise graduate student culminating