664 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" Postdoctoral positions in United States
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of computational biology, Genomics, machine learning, and data science, contributing to the development and evaluation of advanced algorithms for analyzing large-scale biological datasets. This role is ideal
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). The work spans: Close collaboration with researchers in neuroscience, engineering, and ethics. Human-in-the-loop brain/muscle-machine experimentation focused on learning pedagogy for adaptive neural systems
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in Machine Learning, Computer Science, Electrical Engineering, Geophysics, Applied Mathematics, or a closely related field. Demonstrated strong research skills, evidenced by high-quality publications
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. Responsibilities may include: Designing and conducting studies on the clinical impact of GLP-1 and other metabolic therapies Developing and applying computer vision and machine learning techniques to analyze
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 4 hours ago
inventories) with satellite remote sensing data (e.g., spaceborne lidar and/or hyperspectral observations) and apply machine learning and deep learning approaches to address these questions. This position is
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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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Intelligence (AI) and Mathematics. This position involves applying machine learning tools, particularly reinforcement learning (RL), to address complex research-level mathematical problems and developing new
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original research on clandestine printing networks using computational tools Contribute to publications in both AI and humanities venues (machine learning conferences and book history journals) Contribute
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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
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necessary to become credentialed as a Principal Investigator Applying a broad range of statistical and machine learning methods to human performance data collected in real-world settings Developing