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experience with deep learning (DL) methods. Demonstrated proficiency in Python and machine learning frameworks (e.g., PyTorch, Jax, scikit-learn) applied to genomic/related datasets. Experience with sequence
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ability to register with Agile/Fleet Commander . Preferred Qualifications Competence with life cycle assessment methods and software, including uncertainty methods, spatialized approaches, and integration
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are especially encouraged to apply. Experience in advanced numerical methods is preferred, but not required. Applications should include: Complete CV Publication List Research Statement (1-3 page) 3 letters
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ability to register with Agile/Fleet Commander . Preferred Qualifications Competence with life cycle assessment methods and software, including uncertainty methods, spatialized approaches, and integration
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of socioeconomic data, including household survey data and/or other qualitative or quantitative research methods. Evidence of scholarly productivity, including peer-reviewed publications. Preferred Qualifications
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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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-insect community as a model for hypothesis testing in molecular evolution. The individual will use methods such as RNA-seq to identify genes and regulatory sequences that drive plant toxin detoxification
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the university. CAROW connects research on work with the practice of putting it to use. CAROW provides a platform for new interdisciplinary approaches, innovative methods, and nimble resourcing with
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collaborate on projects conducted at the Cornell Lab and with partners to support their career development. Anticipated Division of Time: Develop methods and quantitative workflows using Cornell Lab data
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, or a closely related field. Successful candidates will demonstrate strong training and experience in qualitative and/or quantitative research methods. This is an academic, full-time, non-tenure track