122 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" Fellowship research jobs in United States
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, or Stata); · Creating and managing very large datasets; · Machine learning skills. Basic Qualifications A Ph.D. in any business discipline, organizational behavior, economics, statistics, environmental
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of References Allowed Keywords statistics, biostatistics, computer science, economics, health care policy, causal inference, machine learning
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Apply Now How to Apply To be considered for this position, applicants should submit their materials to Interfolio https://apply.interfolio.com/179185 Cover letter (1-2 pages) which includes a
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contributions are valued and your growth is encouraged. This fellowship is ideal for enthusiastic, team-oriented individuals eager to learn and make meaningful contributions to the field of emerging infectious
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Fellowship position is available in the RSP Lab led by Dr. Sklavenitis Pistofidis. The RSP Lab (https://rsplab.org ) leverages advanced multi-omic technologies, coupled with experimental perturbations and
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collaborative, impact-focused problem solver who wants to be part of a dynamic team. Information about the Shih Lab: Learn more about the innovative work led by Dr. William Shih here: https
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experience in Stata, R, SAS or similar statistical programming languages Prior experience with machine learning and natural language processing is a plus. Application Instructions: To be considered, please
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or similar statistical programming languages Prior experience with machine learning and natural language processing is a plus. Application Instructions: To be considered, please submit the following items
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 8 days ago
be predicted using machine learning based on drug-specific information, patient demographics, and clinical trial data. 2. Modeling for Regulatory Science – Leveraging drug development and regulatory
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individual to join this exciting collaborative research program that builds upon existing strengths in ADRD biology and genetics (JAX) and bioinformatics methods and machine learning approaches (Roux