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modern machine learning approaches with large-scale biological data to automate genome curation by detecting, interpreting, and correcting structural errors, reducing manual effort from weeks to minutes
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Responsibilities will vary depending on the Fellow’s background, but may include: Developing machine learning, optimization, or simulation models to improve clinical operations and resource allocation Advancing
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: machine learning or deep learning, structural modeling and analysis, and genome or transcriptome analysis; have a strong record of peer-reviewed publications or equivalent scholarly output; collaborate
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) for data analysis, numerical modeling, or machine vision Supervision Exercised This position carries a high degree of independence. The Postdoctoral Associate will meet with the PI at least twice per week
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-generation sequencing data Programming skills (e.g., Python, R) for data analysis, numerical modeling, or machine vision Supervision Exercised This position carries a high degree of independence
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Ph.D. in molecular biology, cell biology, biochemistry or neuroscience. Prior research experience in mouse models, and/or membrane trafficking and lysosome biology will be a plus. Interested candidates
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appreciation of others will help ensure the lab operates smoothly and remains a space for meaningful scientific discovery. Prior research experience in neuronal and immune cell model systems, the fields
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, embracing failure as a learning opportunity, and continuously enhancing our knowledge and methods to tackle local, national, and global challenges. The postdoctoral associate will work directly with both
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and sizes are altered by polyploidy in response to stress; be able to collaborate with teams working on other organisms (yeast, Drosophila , duckweed, Chlamydomonas , and Arabidopsis) and modelers