967 machine-learning "https:" "https:" "https:" Fellowship positions in United States
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imaging techniques and flow cytometry to address key questions. The prospective candidate should have the knowledge to perform cell culture works and a willingness to learn and trouble shoot new techniques
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skills are required. Required: U.S. Citizenship or Permanent Residency This position is funded by an NIH T32 grant. Candidates must meet appointment eligibility criteria as outlined https
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information about eligibility, terms of the fellowship, and how to apply, please visit: https://as.cornell.edu/research/klarman-fellowships Application deadline: 15 October 2026 Cornell is an equal opportunity
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. Must be able to work at a computer for extended periods for data analysis, and manuscript preparation. May require routine work with biological samples, including recombinant proteins and antibodies. Use
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Benefits: This position is salaried and benefits eligible. Information regarding postdoctoral fellow salary, which is determined by the number of years post PhD, and benefits can be found at https
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statewide and national learning networks (e.g., National Child Traumatic Stress Initiative Evaluation Community of Practice, Utah Early Childhood Mental Health Workforce). Develop mentoring, and supervision
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are meaningfully engaged and have equal opportunity to succeed. Interested applicants are encouraged to visit cazalas-group.org to learn more about our goals, research, and your academic/research fit. Submission
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Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), particularly in Natural Language Processing (NLP) and Computer Vision (CV) Familiarity with genomic and bioinformatic databases
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tradition, to maintain the excellence of the University, and to offer our students richly varied disciplines, perspectives and ways of knowing and learning. The Keough School is committed to fostering a
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Experience in one or more of the following areas is preferred: Statistical genetics Human genetics Population genetics Evolutionary genetics Bayesian statistics Machine learning Large-scale genomic data