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research, including data collection analysis, and interpretation preferred. Strong oral and written communication skills. Ability to work independently and collaboratively within a research team. Application
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producers. Anticipated Division of Time Research 50% Data analysis 40% Writing 10% Requirements Ph.D. in Animal Science, Dairy Nutrition, Feed Science, or a related field. Strong background in ruminant
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relevant AI/ML/related techniques and genomic data analysis. Background in and motivation for genomics or/and biodiversity conservation studies. Strong background in AI/ML fundamentals and extensive
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such as Cornell’s Transportation Environment Analysis Model for Cities (Cornell TEAM-Cities), the emerging urban transportation, environment, and community health modeling Hub (uTECH Hub), and the
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research, including data collection analysis, and interpretation preferred. Strong oral and written communication skills. Ability to work independently and collaboratively within a research team. Application
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in the biological sciences and experience in molecular genetic techniques, including high throughput sequencing and data analysis. Strong research background with comparative evolutionary questions
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implementation: 50% Literature review: 15% Workshop development: 15% Data analysis and writing: 20% Requirements PhD in natural resources, environmental sciences, environmental policy, ecology, sustainability
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: Record of impactful scientific publications Experience designing and carrying out synchrotron x-ray experiments Proficiency with scientific software for advanced data analysis and/or experimental control
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Systems Engineering (Health Systems Data Analysis, Modeling, Computing, and Cloud-Based Health Systems Analytics and Decision Support Platforms) as part of the CTECH Postdoctoral Fellows program. This
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for conferences, grant proposals Data analysis and pipeline development Technology-based solutions for advancing population health To be eligible, candidates must: Have received a PhD in computer