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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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to work on Bioinformatics and Computational Biology in Cancer Genomics and Immunology. This position will be involved in the development and/or application of computational approaches to understand
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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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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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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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candidates to work on Bioinformatics and Computational Biology in Cancer Genomics and Immunology. This position will be involved in the development and/or application of computational approaches to understand
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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
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using deep learning, computational chemistry, medicinal chemistry, chemical biology, and molecular cell biology to develop novel therapeutics to tackle complex diseases such as cancers. Successful