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, 120-128. Basic Qualifications A PhD (or equivalent), or near completion, in a relevant field such as chemistry, physics, materials science, or engineering (chemical, electrical, or mechanical
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signal processing algorithms on FPGA, optimized to significantly improve the resolution of real-time energy measurements made by the ATLAS Liquid Argon Calorimeter system. Use novel high-level synthesis
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implementing quality assurance and quality control (QA/QC) test Designing testbenches, and contributing to firmware programming for state-of-the-art FPGA architectures, primarily Intel FPGAs. Collaborating
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, such as GPUs and FPGAs, to offloading applications in a seamless and portable way. This includes implementing runtime logic and resource scheduling strategies that can leverage available hardware
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of using FPGA tools and provide them the necessary training to use the ASIC tools. The team at Cambridge consists of three investigators: Prof. Robert Mullins (PI), Prof. Timothy Jones and Dr Rika Antonova
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, PSIM, Proteus, LabVIEW, SketchUp, SolidWorks, etc. Knowledge of microcontrollers, STM32, FPGAs, etc. Knowledge of communication protocols such as I2C, SPI, Profibus, Modbus, CAN, MQTT, and HTTP
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for seizure detection. These algorithms will be implemented on a spiking neural network (SNN) processing unit deployed on FPGA and custom-designed chips with an integrated detection mechanism. Research area and
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developing integrated systems to automate the acquisition and interpretation of neutron scattering data. You will contribute to the development of FPGA tools for real-time processing of live neutron
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. · A PhD (or be close to submission) in atomic physics or a closely related area. 2. Experience · Experience in conducting high quality academic research. · Demonstrable ability
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of biological brains. Spiking neural networks (SNNs) can offer increased processing speed and reduced power consumption, especially when implemented on dedicated hardware (neuromorphic chips or FPGAs). Standard