13 molecular-modeling-or-molecular-dynamic-simulation-"Prof" PhD positions at Duke University in United States
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) and HIV-1 infection dynamics in human lymphoid tissue. New mathematical models will be informed by longitudinal experimental data, including multiplexed spatial proteomics, transcriptomics, collected
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simulations and perform computational experiments using high-level programming languages (e.g., Python, MATLAB, R, or Julia). Curate and integrate experimental data to calibrate and validate models, including
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to therapies and vaccines against human diseases. We are a team of highly interactive investigators that have expertise in immunology, molecular biology, virology, microbiology, structural biology, computational
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available to conduct research on chronic pain. Using animal pain models, behavioral tests, calcium imaging, patch-clamp, optogenetics, neuroanatomy, molecular biology, the successful candidate is expected
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. in microbiology, immunology, systems biology, molecular biology, computational biology, or a related field. • Experience in one or more of the following areas: microbiome research, animal models
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/PhD Degree Experience Background in cardiac biology, neurobiology, bioengineering, or related field Molecular biology Background in electrophysiology preferred but not required Skills Skills in mouse
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of immunology and molecular biology preferred. Candidates should have excellent scientific writing and strong oral communications skills Experience in leading research papers for publication and data
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in the areas of long-term care, whole-person care models, and caregiver support. Minimum Requirements: Eligible candidates should have a doctoral degree (Ph.D. or equivalent) in any of the following
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inhalation exposures. We use in vitro, rodent, and non-rodent models to achieve our study objectives. We will employ various techniques/methods such as ELISA, multiplex MSD panel assays, flow cytometry, bulk
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position is funded by multiple NIH projects, e.g., https://tinyurl.co m/ysxhmujvThe overall goal is to : (1) develop inference and dynamic prediction models using a wide variety of data, including clinical