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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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, Computer Science, Experimental Subatomic Physics, or related fields. Relevant research experience in firmware programming (e.g. with VHDL) and resources optimization for specific FPGA architectures
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topics. Candidates should have some experience working with FPGAs as well as an understanding of computer networks. Experience with both RTL and HLS design is favoured. The ideal candidate would have some
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multilayer PCB, FPGA programming, embedded systems, and preferably ASIC-design. Knowledge in Systems Engineering, particularly in Space and Defence is highly regarded. You will also demonstrate personal
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emulation software for FPGA-based electronics. • Experience contributing to data taking of large experiments. Additional Information: Application Instructions Applications should include: • A curriculum
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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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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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, 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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communication skills. · Skills relevant to the trapping and manipulation of ultracold gases. Examples include basic electronics, development of FPGA devices, development of narrow-linewidth lasers, laser
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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