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of high-throughput systems is maximized using design of experiment (DoE) approaches in order to efficiently explore the high-dimensional parameter space of the electrolysis processes. DoE is required
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. Experience with rapid prototyping tools (e.g., 3D printers, CNC machines, laser cutters, PCB fabrication). Experience in designing and modeling spacecraft, habitat, or mission environments using 3D modeling
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magnetohydrodynamic flow. Experience in design and analysis of fusion reactor components, specifically tritium breeding blankets. General familiarity with a variety of blanket concepts (solid, liquid metal, and molten
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data to design robust, efficient deep learning algorithms that maximize the information extracted from images and delivered to the robot. To be successful in this role, we are looking for candidates
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researchers to support complex behavioral experiments. Qualifications: Experience in designing and building custom optical systems. Familiarity with Zemax and CAD software. Strong programming skills in C
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The School of Engineering and Applied Sciences at Princeton University seeks applications for a postdoctoral position at the Keller Center for Innovation in Engineering Education. The position is in
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layer algorithm design and performance analysis In addition to the above qualifications, it is highly desirable that the candidate has prior research experience in one or more of the following research
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, signal processing, wireless/optical communications, and communication system engineering Extensive experience in physical layer algorithm design and performance analysis Proven track record of publications
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ternary neural networks using FPGA devices. The successful candidate will have significant experience in machine learning, FPGA design and an outstanding track record in conducting machine learning research
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of separation technologies for energy and critical resources. Key Responsibilities: Design, execute, and analyze experiments on capacitive deionization for critical materials recovery. Design and implement