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Field
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will be focused primarily on the development and application of novel computational algorithms to analyze and integrate diverse omics datasets, including single-cell RNA-seq, spatial transcriptomics and
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different disciplines and mentors Stanford Departments and Centers: Medicine, Biomedical Informatics Research (BMIR) Biomedical Data Sciences Postdoc Appointment Term: 1 year minimum with the option to extend
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within the last 0-5 years) in computational science, mathematics, physics, or a related field with a focus on image processing. Proven experience in algorithm and software development. Expertise in Python
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. This work involves developing novel techniques, algorithms, and software packages that enable more robust and scalable approaches to cybersecurity using AI-based techniques. In addition to technical
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unravel the complex relationships between land use changes and fire regimes over the past 60 years. The successful candidate will lead efforts to: Develop advanced deep learning algorithms for classifying
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
looking for a postdoctoral fellow interested in developing either machine learning algorithms for high-resolution histopathology imaging/spatial-profiling data in combination with other modalities (e.g
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of the following: Ecosystem Modeling, Machine Learning, Microbiome, Microbial Ecology, Soil Science, or Computational Biology. The positions are for several different projects, including the following: (P1
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excellent opportunity to work in the development of external relationships and to engage in industry-university partnerships at the cutting edge of engineering, computer and data science, technology, natural
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are currently recruiting motivated Postdoctoral Fellows with a Ph.D., M.D. (or equivalent) and a background in Computer Science, Electrical Engineering, Biomedical Engineering, or related fields. Candidates who
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. This includes integrating LLMs with structured data sources to develop robust computational phenotyping algorithms and scalable models for real-world evidence generation. The role will involve both method