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Bioconductor. *Strong foundation in statistics, data analysis, and computational methods; familiarity with machine learning and algorithm development is desirable. *Experience working in Unix/Linux computing
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Institute, the successful candidate will join a small, collaborative group of staff scientists embedded within the Curtis Lab, working at the intersection of cancer genomics, computational biology, and
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research. The ideal fellow will be interested in developing and applying novel computational algorithms to novel datasets generated in the setting of non-neoplastic and neoplastic disease. Key
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captures neural activity and intelligent behavior at unprecedented scale and resolution. This ambitious project spans multiple institutes including the Wu Tsai Neurosciences Institute, Stanford Bio-X, and
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multiple schools and centers at Stanford University as well as external collaborations with other universities and municipal and non-profit partners. This highly collaborative environment provides many