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The University of Oxford is seeking a highly motivated Postdoctoral Scientist with expertise in biostatistics, machine learning, and cardiac magnetic resonance imaging (MRI) to join Professor Betty
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Associate will contribute to ongoing projects and have the opportunity to develop independent research aligned with the aims of the ADN lab. Current work focuses on machine learning and multivariate decoding
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, biologics, and cannabis. Apply statistical and machine learning approaches (e.g., sequence analysis, latent class analysis, clustering) to examine medication use trajectories and patient subgroups
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Full time, Fixed Term for 3 Years The closing date for applications is 23.59 on 4th August 2025 Postdoctoral Researcher in Gut Microbiology (Food Microbial Sciences Unit, Department of Food and
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in Operations Research. Strong skills in Machine Learning. Practical experience in mining or industrial environments (through projects, thesis, or postdoctoral work). Ability to work effectively in a
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
Experience with Knowledge Graph machine learning including KG embeddings. Strong background in data integration, ETL pipeline design, and schema transformation. Familiarity with workflow managers, with a
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. Job description: - first-principle modeling and simulations of electrolytes - development of new machine learning strategies and quantum simulation approaches - application of specially developed
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We are seeking a Postdoctoral Researcher in Human-AI interaction to join a research group focused on studying learning and decision-making in humans and machine learning systems led by Prof Chris
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) and spatial Machine Learning (ML) models Salary and full employee benefits are offered in accordance with Princeton University guidelines. The Term of appointment is based on rank. Positions
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data, identifying structural errors in the dataset, and for maintaining a record of all steps from data extraction to dataset assembly · Fitting of machine learning models · Development of instrumental