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on data science and engineering. The scientist will collaborate with Princeton and GFDL researchers to enhance, analyze and deliver high-resolution earth system model data, with an emphasis on Seamless
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genomic data for reconstructing evolutionary patterns and processes that have shaped biological history across deep timescales. The ideal candidate will have a background in phylogenomics and bioinformatics
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data-driven, computational approaches. Successful candidates will be willing and able to work across a breadth of disciplines - from genomics to computer science, sociology to psychology, engineering to
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psychology. Initial appointment is for one year with the possibility of renewal based on satisfactory performance and continued funding. Essential Qualifications: PhD in a relevant field. Interested
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codebases and data pipelines; ensure reproducibility and version control *Work with team members to integrate LLM modules into user friendly decision support platforms *Facilitate user testing and gather
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. Qualifications: Successful candidate will have a Ph.D. in Philosophy, Religion, or a related field and must have less than three years of post-PhD research experience prior to anticipated start date. Applicants
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on projects related to machine-learning for mass spectrometry-based metabolomics data. Positions are available starting July 2024, and will remain open until excellent fits are found. Successful candidates will
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should submit a curriculum vitae, a publication list and a research statement, and provide contact information for three references by November 1, 2024 11:59 (EDT). Candidates may also include a cover
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and mathematical approaches to signal analysis, information theory, computational biology and image processing. The term of appointment is one year with the possibility of renewal pending satisfactory
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research on relationships between exposure to COVID mitigation policies and a range of life outcomes in mainland China. Successful applicants should have outstanding data management skills, strong