665 machine-learning-"https:"-"https:"-"https:" Postdoctoral positions in United States
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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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regional leadership in biostatistics, genomics, biomedical informatics, artificial intelligence and health data science. The Postdoctoral Associate will conduct research in statistical machine learning and
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the performance and scalability of large-scale molecular dynamics simulations (e.g. LAMMPS) using machine-learned potentials (e.g. MACE) through algorithmic improvements, code parallelization, performance analysis
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that apply: ☒Sitting at computer work station for long periods of time ☒Standing for long periods of time ☒Risk of back injury from moving/lifting, equipment, or materials ☐Repetitive motion ☒Lifting/Carrying
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development of new AI-driven curricula and assessment frameworks for health professions education. Design, develop and apply AI solutions including traditional machine learning models, natural
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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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candidates will have strong past experience in scientific machine learning. Experiences in large language models, molecular modeling and simulations, and/or polymer science are preferred but not required
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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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Professor Fei Lu and Bloomberg Distinguished Professor Mauro Maggioni on topics including mathematical foundations of data science and statistical/machine learning, with an emphasis on inverse problems and in
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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