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and/or case-crossover analyses, and have a promising publication record. Expertise in one or more of the following areas: statistical analysis of large data sets, machine learning, data visualization
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. • Expertise in one or more of the following areas: statistical analysis of large data sets, machine learning, data visualization, and a high level of independence with a publication record to support these
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the intellectual and creative life of the department and Cornell at large. We are interested in recruiting candidates who demonstrate commitment to the highest standards of scholarship, teaching, and professional
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, spatial statistics, machine learning approaches, large ecological datasets Previous experience with passive acoustic monitoring and/or eBird data Familiarity with ecology and/or ornithology Demonstrated
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eligibility. Application Procedures The following application materials must be submitted via Academic Jobs Online position #32526 by October 15, 2026. Information Cover Sheet (will be generated via your AJO
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depth in some of the following areas (not all are required): Large-scale data analysis and learning analytics methods Experimental or quasi-experimental design; validity and measurement Working with LLMs
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genomics modalities Background in immunology or inflammatory disease Experience with machine learning methods applied to large data Background in immunology, infectious disease, or inflammatory disease
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eligibility. Application Procedures The following application materials must be submitted via Academic Jobs Online position # 32526 by October 15, 2026. Information Cover Sheet (will be generated via your AJO
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commitment to working with highly engaged students. Exceptional attention to detail, organizational skills and ability to handle confidential information with integrity. Flexibility to adapt and improve
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. The postdoctoral associate will be expected to work both collaboratively and independently on research projects, advancing computational methods using machine learning, developing automated pipelines for data