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mechanisms at molecular and cellular levels. Postdoctoral fellows will join a supportive, inclusive environment and contribute to exciting research while developing their own independent research programs
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* Applicants should have a medical degree (M.D., M.B.B.S., or equivalent.) Translational projects will require the postdoctoral fellow to work with faculty to plan study methods, draft IRB protocols, and carry
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degree (M.D., M.B.B.S., or equivalent. Translational projects will require the postdoctoral fellow to work with faculty to plan study methods, draft IRB protocols, and carry out the project as detailed. It
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Transportation Program, aimed at addressing power quality challenges in electric vehicle (EV) charging infrastructure. The selected candidate will contribute to the modeling, simulation, and prototyping
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. Responsibilities* The successful candidate will be able to plan, develop, execute, and analyze an independent research project in the context of a supportive lab and institutional environment. In addition, they will
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junior lab members Proficiency in Microsoft Office, GraphPad, and other scientific software programs Desired Qualifications* Molecular biology, Cancer biology, and immunology research lab experience
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, statistics, computational biology, or a related field; Strong programming skills in one of the traditional programming or scripting languages (R/Python/C++); Experience working in a Linux environment; A strong
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expertise in Robotics, 3D Motion Capture, Gait Analysis, Programming (e.g. Matlab, LabView, etc.), and scientific writing skills are desired and would be highly beneficial for this position. In
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communication skills. Experience in coding with Python, MATLAB, Julia, C/C++, or a similar program language. Experience with biological data analysis. Knowledge of network science and/or complexity sciences
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. Experience in coding with Python, MATLAB, Julia, C/C++, or a similar program language. Experience with biological data analysis or simulations of dynamics. Knowledge of network science and/or complexity