35 computational-physics-"https:"-"https:"-"https:" Postdoctoral positions at Carnegie Mellon University
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, faculty members, researchers, and students are revolutionizing focus areas in advanced manufacturing, bioengineering, computational engineering, energy and the environment, product design, and robotics. In
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of materials science and physics and such other tasks that are assigned to you. Core Responsibilities: Design and fabricate novel materials and devices using computational modeling and nanofabrication Develop
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, faculty members, researchers, and students are revolutionizing focus areas in advanced manufacturing, bioengineering, computational engineering, energy and the environment, product design, and robotics. In
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well as familiarity with optical and fluorescence microscopy and common techniques of physical biochemistry. While the research is focused on biological sciences, the researcher will work in an automated science lab
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, faculty members, researchers, and students are revolutionizing focus areas in advanced manufacturing, bioengineering, computational engineering, energy and the environment, product design, and robotics. In
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, faculty members, researchers, and students are revolutionizing focus areas in advanced manufacturing, bioengineering, computational engineering, energy and the environment, product design, and robotics. In
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combine everything from physics and chemistry to civil and electrical engineering. The Materials Science and Engineering Department in the College of Engineering is seeking a postdoctoral scholar to join
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curious to deliver work that matters, your journey starts here! The Civil and Environmental Engineering Department at Carnegie Mellon offers a unique interdisciplinary program that enables you to develop
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Responsibilities Include: Develop computational methods for inference and control that improve the reliable and efficient operation of autonomous agents in complex, uncertain environments. Modeling dynamical systems
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for the Pitkow Lab. Core Responsibilities Include: Develop computational methods for inference and control that improve the reliable and efficient operation of autonomous agents in complex, uncertain environments