18 big-data-and-machine-learning-phd Postdoctoral research jobs at Harvard University
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is currently building and commissioning a network of calibrated multi-sensor observatory-class systems, and developing novel machine learning methods with the aim of collecting science-quality data
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areas: Generative AI Agentic AI Graph Representation Learning and Modeling Foundation Models Large Language Models Multimodal Learning Basic Qualifications A Ph.D. or equivalent degree in Machine Learning
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pages, check your computer’s network connection. If your computer or network is protected by a firewall or proxy, make sure that Firefox is permitted to access the web. You can continue with your default
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additionally learn a cutting-edge technique called cyclic immunofluorescence (CyCIF), which allows spatial resolution of different cell states within a tissue in order to understand how tumors are organized and
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of biomechanics and computer vision to document the wingbeat frequencies and phototactic behaviors of diverse insects under diverse contexts. Candidates will be expected to plan and lead behavioral experiments
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. Successful candidates will be expected to contribute to technique development/material synthesis, plan and lead research projects, acquire and analyze experimental data, supervise and mentor undergraduate
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characterize gene transfer agent diversity, regulation and species-specificity across the Tree of Life. For further information on the lab, please visit: www.gozzilab.org More broadly, bacteria constantly face a
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Qualifications PhD in Developmental Biology, Cell Biology, or related fields. Strong background in cell biology, different types of fluorescent imaging, and organoid culture required. Experience with model
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year, renewable if additional funding resources are secured. Basic Qualifications The successful applicant should have PhD in Linguistics, and extensive research experience on some of the following
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the Department of Environmental Health at Harvard University has openings for highly motivated postdocs to develop and apply novel computational approaches for next generation sequencing data analysis