72 big-data-and-machine-learning-phd-"https:" Postdoctoral positions at Duke University
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is a single entity that integrates and aligns research and patient care with the goals of decreasing the burden of cancer and accelerating scientific progress. This position requires a PhD in cell
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learning new and innovative methods. Flexibility in working across a wide array of projects and subject matter. Interest in assisting with recruitment and data collection of ongoing trials and project
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for genomics (e.g., generative models, transformers, agentic workflows) and/or statistical learning (e.g., network & spatiotemporal modeling, functional/longitudinal data, time-series). Analyze single-cell
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Education/Training: Work requires a PhD degree in biochemistry, molecular biology, pharmacology or other science related scientific field. Experience: None required above education/training requirement
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/time management skills, and interest in interdisciplinary collaboration. • Experience in the use of large language models for analyzing text and/or advanced skills involved in analyzing complex
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, gender identity, genetic information, national origin, race, religion, sex (including pregnancy and pregnancy related conditions), sexual orientation or military status. Duke aspires to create a community
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, disability, gender, gender expression, gender identity, genetic information, national origin, race, religion, sex (including pregnancy and pregnancy related conditions), sexual orientation or military status
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Position Description The Signal Inference, Information and Learning (SIIL) Group is seeking a Postdoctoral Researcher to perform research in the area of statistical signal and array processing with
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functional genomics Cell and molecular biology Neuroscience or neurobiology Bioinformatics, computational biology, and large-scale data analysis Choose Duke. Successful candidates will join a collaborative and
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material damage assessment 3) Developing AI and machine learning models for robot-assisted laser surgery and validate the model by comparing the results to experimental observations. 4) Support the