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. Xiaohua Liu’s Lab at the University of Missouri (MU), Columbia. The research focuses on the development of biomimetic 3D biomaterials designed to regulate cell-material interactions and promote
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comprehensive benefits package to eligible employees, based on position, which includes: Medical, prescription drug, and dental coverage Paid vacation, holidays, and various leave programs Competitive retirement
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team environment. Excellent written and oral communication skills, with a strong publication record in peer-reviewed journals. Experience in computational modeling or structure-based design is desirable
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and conduct in vivo experiments using murine models of liver injury, fibrosis, and regeneration, including surgical procedures and drug administration • Perform high-parameter flow cytometry
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Preferred Qualifications: We are interested in candidates with general research experiences in quantum optics and quantum information science. Priority is given to candidates with experience on the design
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collaborative culture, with access to cutting-edge computational and experimental resources in one of the nation’s top NCI-Designated Comprehensive Cancer Centers.
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research, collaborate with leading scientists, and play a key role in laboratory operations and scientific discovery. Duties and Responsibilities This position will design and conduct experiments that drive
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Missouri University of Science and Technology | Rolla, Missouri | United States | about 2 months ago
. Xiaohua Liu’s Lab at the University of Missouri (MU), Columbia. The research focuses on the development of biomimetic 3D biomaterials designed to regulate cell-material interactions and promote
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, clinicians, and scientists with unparalleled experience who advance medical innovation and provide patient care informed by the latest research findings. We offer an outstanding education in medicine
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to publications and grant writing, and support the supervision of students and junior researchers in one the following research areas: Computational oncology AI drug discovery Statistical genetics, single-cell