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Field
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Full time: 35 hours per week Fixed term contract: Until 31st July 2026 with potential for a further extension subject to funding The opportunity: Where curiosity becomes impact. With roles from hospitality to research, there’s a career for everyone at the University of Edinburgh. We can offer...
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Sonmez Turan meltem.turan@nist.gov 301.975.4391 Description NIST standardized cryptographic algorithms are intended to be "bulletproof". That is, the computational complexity needed to break them is
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-acquisition circuitry, and signal-processing/pattern-recognition algorithms. The sensors must be tailored for the particular nature of a given chemical or biochemical measurement problem by optimizing and
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development programs where you will work with a multi-disciplinary team of scientists and engineers performing a wide range of applied research and development projects involving sensors, lasers, and electro
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learning methods and statistical analysis, to effectively exploit data collected from sensors and acquisition systems. Particular attention is devoted to algorithm robustness, uncertainty management, and
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Ph.D degree in electrical engineering, computer engineering, computer science, or a related discipline Demonstrated experience developing, training, and applying AI algorithms to physical sensor data
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develop digital twin algorithms to assist clinicians in developing treatment plans for patients with chronic diseases. Analyzes complex sensor data, works with a multidisciplinary team to develop health
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-on experience building or modifying equipment and working effectively within multidisciplinary teams. Experience with Linux, ROS/ROS2, electronics and sensor integration, algorithm design, and laboratory
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Novel wavefront sensors with Optical photon-counting detectors). This can be achieved by improving detector performance and implementing new signal processing algorithms. Led by Paris Observatory, AMINO
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developments in sensor design, dataset transmission, data analysis, and numerical modeling to distinguish between normal and abnormal features. Here, the goal is to develop machine learning algorithms