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Field
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appointment). Strong background in statistical or machine learning methodology, optimization, or high-dimensional data analysis. Proficiency in R or Python; experience with deep learning, causal inference
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The Postdoctoral Fellow is expected to work both independently and in close collaboration with the Principal Investigator (PI) Dr. Ayalew Osena and students to optimize Agrobacterium transformation and improve
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and present results at peer-reviewed venues and conferences. Example project areas Performance analysis and optimization of end-to-end scientific workflows, including those originating at DOE facilities
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measurements in spine surgery, metabolomic/genomic factors on pre-optimization and post-surgical outcomes, and various clinical and radiographic factors associated with surgeries for adult degenerative and
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execute experiments that advance the frontier of self-optimizing microscopy, including automated alignment, adaptive focusing, drift correction, and AI-assisted atomic structure recognition. The role
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agentic orchestration frameworks for multi-step, multi-instrument experimental workflows (e.g., observe–reason–plan–act). Design closed-loop optimization and active learning strategies for real-time
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Science , Data Science and Information , Data Visualization , Deep Learning , High dimensional Data , Large Language Models , Large Scale Optimization , Machine Learning , Natural Sciences , Public Interest
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with multi-disciplinary teams. Key Responsibilities: • Lead bioinformatics efforts to identify and validate biomarkers for brain tumors using high-throughput sequencing technologies. • Develop, optimize
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optimize new methods and techniques to address critical project or functional area needs. Participants will improve existing or develop new laboratory methods and processes, read and adapt literature
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reporting procedures for any incident or event that did affect or potentially could affect the project goals and workflow. Optimize protocols and improve methods currently employed. Coordinates work