716 machine-learning-"https:"-"https:"-"https:"-"Linnaeus-University" Postdoctoral positions in United States
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Field
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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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years of post-degree experience. Applicants should address how they would contribute to the research focus of Math+AI. The primary focus of this position is in creating methods and applying Machine
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. QUALIFICATIONS PhD in Civil Engineering, Environmental Science, Computer Science, or a related field Research experience in hydrology, geospatial analysis, and machine learning Skills of scientific writing 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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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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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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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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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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, Electrical and Computer Engineering, or a closely related field. ● Proficiency in MATLAB or Python. ● Prior experience with Neuroimaging analysis (using FSL, AFNI, SPM, etc.) ● At least one
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-of-the-art data management, machine learning and statistics techniques. With the advancement of Exascale systems and the variety of novel AI hardware designed to accelerate both training and inference