580 machine-learning-"https:"-"https:"-"https:"-"https:"-"U.S"-"U.S" Postdoctoral positions in United States
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–based, web-enabled decision-support tool for farmers, using machine learning (random forest) predictive models. Working closely with the PhD student and PIs to ensure rigorous model development
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Postdoctoral Research Associate - Hybrid Computational-Experimental Scientist in Bacterial Drug Resp
to antibiotics and host-like conditions. • Develop and apply statistical or machine-learning methods for interpreting single-cell and genomic datasets. • Work closely with wet-lab scientists to design perturbation
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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crane. The successful candidate will build reproducible machine learning pipelines, integrate detections into spatial ecological models, and generate conservation-relevant outputs for regional partners
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, research, and public service. Job Description Purpose: The Department of Electrical and Computer Engineering and AggieFab Nanofabrication Facility at Texas A&M University seeks a Postdoctoral Researcher to
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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Biology with both high-throughput experimental (proteomics and genomics) and integrative computational (network analysis and machine learning) methodologies, aiming to understand gene functions and their
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Science Statistics / Biostatistics Applied Mathematics Data Science Demonstrated expertise in modern machine learning, including at least one of the following: Deep learning (e.g., transformers, sequence models
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a team-oriented research environment. PREFERRED QUALIFICATIONS Prior experience in one or more of the following areas: longitudinal data analysis, survival analysis, spatial methods, machine learning
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Integrate multi-omics data with clinical, cognitive, and imaging phenotypes in longitudinal cohorts Develop and apply statistical and machine-learning models (e.g., mixed-effects models, survival analysis