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Field
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Because of the drastically increasing demand from AI/ML applications, the computing hardware industry has gravitated towards data formats narrower than the IEEE double format that most computational
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: Design and iterate custom-built single-molecule measurement platforms SWNT FET biosensors. Hardware Integration: Apply basic electrical engineering principles to interface biological sensors with low-noise
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, tape-out, and testing, preferably with applications to AI systems ● Design, analysis, and modeling of AI hardware such as deep neural network accelerators or neuromorphic computing. ● Emerging AI
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, tape-out, and testing, preferably with applications to AI systems ● Design, analysis, and modeling of AI hardware such as deep neural network accelerators or neuromorphic computing. ● Emerging AI
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for greenhouses and vertical farming systems. The successful candidate will focus on robot hardware development, automation system design, machine vision, and intelligent control, supported by physics-based
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involves designing, executing, and analyzing current or future quantum simulations at the intersection of subatomic physics and quantum information science. The successful candidate will also lead peer
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of experimental quantum communication hardware development, optical memory qubit characterization, and fiber-based networking demonstrations using novel memory qubits. The goal is to employ the natural telecom
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embedded systems from vulnerabilities rooted in sensor physics, studying the impact of physical signals (e.g., acoustics, lasers, electromagnetic emissions) on AI and sensing systems, and innovating hardware
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particle trapping and related measurement techniques. Relevant skills include understanding of fundamental principles, design, optimization, computational electromagnetic, mechanical and thermal modeling
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. Role & Responsibilities: Design and optimize large-scale electrophysiological and behavioral experiments using next generation custom-built hardware and software platforms Develop and implement end