32 computional-geometry-algorithm Postdoctoral positions at Baylor College of Medicine
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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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learning algorithms and creating predictive models. This role will contribute to evidence-based processes and procedures for program quality review and assessment of outcomes data as well as develop
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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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a diverse team of biological and chemical scientists Experience with high performance computing environment (HPC) / cluster job submission Knowledge of statistical methods, data science algorithms
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field (e.g. statistics, computer science, or quantitative biology). Experience in the application and development of computational methods/tools or machine learning algorithms. Good computer programming
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in the established CNRC Postdoctoral Training Program which involves forming a mentoring committee, monthly journal club, semi-annual research presentations, and weekly seminar series. For more
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research program integrates state-of-the-art genomic and molecular approaches—including ribosome profiling, CLIP-seq, single-cell multiomics, CUT&Tag, and long-read sequencing—to elucidate gene regulatory
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AI-modeling and coding. The successful candidate will work alongside a multidisciplinary team, leveraging artificial intelligence and computational methodologies to optimize treatment plans, enhance
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computer science. These skills would be applied to large mass spectrometry and mass spectrometry imaging datasets for clinical research, focusing on malignancies including breast and lung cancer and intraoperative
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. The goal of the Training Program at the CNRC is to prepare Ph.D. and M.D. postdocs for biomedical research careers in nutrition that focus on pregnancy, lactation, infancy, childhood, and adolescence