94 condition-monitoring-machine-learning Postdoctoral positions at The Ohio State University
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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efforts will extend to therapeutic modalities including immune checkpoint blockade, cytokine-based immune modulation, and engineered cellular therapies such as CAR-T platforms. The scholar will contribute
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engineering, materials science and engineering, or computer science and engineering. The postdoctoral fellow is expected to actively work with more than one CQISE faculty member at OSU to strengthen
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computer or microscope; frequently stands to perform bench work; occasionally lifts objects up to 20 lbs (e.g., reagent boxes); requires visual acuity to perform microscopy and analyze data. Location
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learning outcomes and instructional practices. The position supports a collaborative research initiative led by faculty and staff from the College of Arts and Sciences and the College of Education and Human
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processing equipment development. The ideal candidate will have experience applying domain knowledge in materials science and engineering as well as machine design and construction to create, with our great
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. Opportunities to teach in the Doctor of Pharmacy (PharmD) curriculum may also be available. Courses could be taught via in-person or distance-based delivery formats, and other teaching duties may be assigned as
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-solving skills, ability to learn new research techniques, and ability to generate new research ideas. Additional Information: The College of Arts and Sciences is the largest college and the academic heart
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related inflammatory conditions. Responsibilities Plan, design, and execute experiments of considerable scope and complexity in cardiovascular and endothelial biology. Investigate mechanisms
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, with a focus on building multimodal AI models to predict dental caries progression. The successful candidate will work on developing deep learning and computer vision models using longitudinal dental