66 computer-engineering Postdoctoral positions at Princeton University in United-States
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Plasma Physics Lab and in the Physics, Geosciences, and Mechanical and Aerospace Engineering Departments, and at the nearby Institute for Advanced Study. The expected start date is September 1, 2026
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advanced microscopy techniques and related methods. Candidates who are nearing completion of their Ph.D. (i.e. with a confirmed defense date) or hold a Ph.D. in chemical engineering, chemistry, materials
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as Computer Science, Robotics, Civil Engineering, Architecture, etc.Excellent track record of research and publications related to the job descriptionStrong scientific writing and communication
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: 277494300 Position: Postdoctoral Research Associate in Microfluidics, Nanofabrication, and Nanophotonics Description: The Department of Electrical and Computer Engineering has opening for postdoctoral
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or Bio Engineering or related area is required. We are interested in candidates who have an interest in: *Advanced Manufacturing and Integration of Scalable Structures*Soft and Living Materials *Natural
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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Department of Chemical and Biological Engineering at Princeton University. The position is in the broad area of renewable energy systems synthesis, analysis, and optimization. The goal of the project is to
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the Andlinger Center for Energy and the Environment and the Department of Chemical and Biological Engineering at Princeton University. The position is in the broad area of renewable energy systems synthesis
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have or be expected to have a PhD with appropriate research experience in computational biology, chemistry, biochemistry, computer science, biological or chemical engineering, forensic science, or a
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials