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advance regenerative medicine. For more information about the lab, please visit https://mesa-lab.org/ .Projects will utilize in vivo mouse models, transcriptomic techniques, and advanced intravital imaging
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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and metastasis, and dietary strategies for the prevention and treatment of cancer. We welcome highly motivated candidates who have or expect to have a Ph.D. degree in the fields of molecular cell
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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and metastasis, and dietary strategies for the prevention and treatment of cancer. We welcome highly motivated candidates who have or expect to have a Ph.D. degree in the fields of molecular cell
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials. Candidates who are nearing completion
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information about the lab, please visit https://mesa-lab.org/. Projects will utilize in vivo mouse models, transcriptomic techniques, and advanced intravital imaging to investigate: 1) How immune cells localize
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treatment of cancer. We welcome highly motivated candidates who have or expect to have a Ph.D. degree in the fields of molecular cell biology, cancer biology, chemical biology, biochemistry, cancer genomics