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. Background: Our laboratory investigates the fundamental mechanisms governing cerebrospinal fluid (CSF) regulation and dysfunction in hydrocephalus, with a focus on mechanosensitive ion channels in the choroid
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focuses on uncovering the molecular mechanisms that regulate cytoskeletal organization and cell adhesive interactions in the ocular lens, with the goal of understanding their roles in lens growth
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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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to identify novel biomarkers and gain a better understanding of mechanisms underlying these inflammatory diseases. All candidates should have a strong interest in population or clinical research, be able
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computational models of the immune response for multi-scale epidemic models. This position offers an excellent opportunity for recent graduates interested in applying quantitative and computational methods
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laboratory notebooks. Monitor progress of research projects and coordinate with Principal Investigator and Program team to stay on budget and schedule to meet the milestones and deliverables. Follow standards
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laboratory notebooks. Monitor progress of research projects and coordinate with Principal Investigator and Program team to stay on budget and schedule to meet the milestones and deliverables. Follow standards
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mathematics, computational biology, quantitative biology, or a related field. Experience in ABM development and ODE/PDE modeling and analysis. Strong programming skills (Python, Julia, Matlab, C++, or similar
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research training program, unless research training under the supervision of a senior mentor is a primary purpose of the appointment. · The appointee works under the supervision of a scholar or a department
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, computer science, bioinformatics, or o ther related disciplines is required. Strong interest, research background and experience in the methodology research in functional data analysi s, tensorregression, high