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MD or Ph.D. in Basic Science, Health Science, or a related field. No experience required. Preferred Qualifications PhD in computational science Working knowledge of machine learning and deep learning 3
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platforms include GRO-seq, RNA-seq, ChIP-seq, ATAC-seq, CRISPR-seq, single-cell RNA/ATAC-seq, microC, machine learning, sophisticated mouse genetic tools, and an ex vivo tissue culture system from patients
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the Texas Children's Cancer Center. The project aims to develop and test novel CAR-redirected immunotherapy for pediatric solid tumors. In particular, we want to design and test a regulated PRDM1 knockdown in
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/tools or machine learning algorithms. Good computer programming skills in R/Matlab/PerlPython. Knowledge of basic molecular biology, genomics, and epigenetics. Experience in next-generation sequencing
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; multi-level regression, semi-parametric models, latent variable modeling, and machine learning methods. Ability to use different software to address the increasing complexity of health-related data