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development and application of deep learning methods, with a strong interest in understanding molecular mechanisms of disease. The position will be highly collaborative working with a diverse group of
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, bioinformatics, signal processing, machine learning, and related fields are encouraged to apply. The fellow will play a key role in advancing the objectives of the R01, contributing to the development
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single-cell transcriptomic data. The candidate should be proficient in, or highly motivated to learn cancer data science, machine learning, and high throughput sequencing analysis. Successful applicants
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at the time of hire. PhD in Computer Science, Artificial Intelligence, Computational Linguistics, Machine learning, Computer Engineering or related fields Preferred Qualifications: Experience in developing and
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regulations Excellent written, verbal, and oral communication skills Ability to interpret and master complex research protocol information Highly motivated, eager to learn, taking initiative and excellent
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offers a diverse and inclusive learning environment that fosters innovation, growth, and the holistic development of its students. The work location for this position is on-site. This position is Exempt
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and focused on scientific investigation, lifelong learning, and a balance of personal and professional values. In addition to a vibrant and highly competitive residency program with 25 positions, we