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, and other methods. Collects data using cameras, sensors, and data acquisition systems. Prototypes, troubleshoots, and fine-tunes machine learning models for outcome predictions relevant to surgery
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implements machine and deep learning programs. Develops algorithms to deconvolve RNA-seq data and compare them to AI-based methods. Performs follow up validation efforts on cell lines. Minimum Qualifications
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research protocols and procedures, including in computational tasks where data visualization, preprocessing, or interpretation can be improved. Devises and deploys custom machine learning approaches where
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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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candidates with a proven track record in developing open-source machine learning, deep learning, or cheminformatics tools (Preferred written in Python). Job Duties Plans, directs and conducts research
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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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models and machine learning. Experience in epigenetics or gene regulation is a plus, and experience with statistical analysis tools such as R or Python is recommended (successful candidates will be
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machine learning approaches where applicable. Provides feedback and guidance to wet-lab scientists on experimental design. Summarizes research findings and publish results in research journals. Assist with
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, national, and international meetings, and reading current primary literature. Provides a friendly working environment and be available to teach laboratory techniques and scientific methodology to research