13 computer-aided-design Fellowship positions at Indiana University in computer-science
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Posting Details Position Details Title Institute for the Study of Contemporary Antisemitism (ISCA) / Borns Jewish Studies Program - Visiting Research Fellow Appointment Status Non-Tenure Track
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of Biostatistics and Health Data Science at Indiana University School of Medicine, in close collaboration with the Regenstrief Institute, a nationally renowned center for health informatics research and real-world
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questions, advancing deep learning models, or other topics discussed with the PI. We use publicly available and simulated genomic data. Core job duties include: (1) Building computational pipelines
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with both molecular biology and synthetic chemistry is preferred but not required. Familiarity with computational design tools, organometallic catalysis/methodology development, enzyme library generation
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for spatial population genetics. Our research integrates custom neural architecture design, simulations, and publicly available genomic datasets to develop new inference methods. The Postdoctoral Associate will
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required for enzyme production and engineering, synthesis of substrates and product standards, high throughput screening of enzyme libraries, and application of computational protein design tools. Careful
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of Biostatistics and Health Data Science at Indiana University School of Medicine, in close collaboration with the Regenstrief Institute, a nationally renowned center for health informatics research and real-world
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, omics, physiological signals, clinical notes), and causal AI (causal inference, discovery, counterfactual reasoning). The successful candidate will collaborate with an interdisciplinary team of computer
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applications for a postdoctoral research position. The position involves the design and synthesis of hybrid molecular and nanomaterial catalysts (see here for recent work: ACS Appl. Bio. Mater.https://doi.org
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: (1) Designing, implementing, and overseeing research projects focused on methods development for understanding genetic variation within and between populations, (2) building computational pipelines