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Field
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Qualifications Experience with Matlab, Phyton, C++ or other programming languages. Experience with remote sensing (multispectral and thermal IR). Experience with LiCOR measurements. Experience with machine
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, or machine learning experts to create predictive virtual 3D mammalian embryos for human health, especially congenital heart diseases. We welcome applicants with expertise in genomics, developmental biology
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understanding of artificial intelligence applications and methodologies, such as working knowledge of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI
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understanding of neuroscience but also advanced technical expertise in machine learning, artificial intelligence, and data modeling approaches. Responsibilities: Conduct research on the mechanisms underlying
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, generous retirement benefits, and a wide array of family-friendly and cultural programs to eligible team members. Learn more at https://hr.duke.edu/benefits/ Duke is an Equal Opportunity Employer committed
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training and research. MINIMUM QUALIFICATIONS PhD in neuroscience, neurobiology, machine learning, biomedical engineering, or related field. Demonstrated experience independently executing all phases of a
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 12 days ago
. Description: The post-doctoral fellow will apply machine learning and/or other data science techniques to conduct scientific research, improve remote sensing data products and analysis, integrate machine
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contextual modulators of executive control like emotion and motivation. We use and develop advanced computational methods, including 'big data' statistical methods, machine learning, and artificial
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Experience with molecular biology techniques and cell culture Willingness to learn new techniques and skills Ability to independently design and conduct experiments Willingness to think critically A passion
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will develop novel statistical and machine learning methods for any of the following: multi-omics data (such as bulk and large-scale single-cell RNA sequencing data, spatial transcriptomics, bulk and