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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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hardware design (Verilog/VHDL), FPGA-based acceleration, etc. Experience with deep learning frameworks like PyTorch, Keras, or TensorFlow, and tools such as Jupyter Notebook, is expected. A strong foundation
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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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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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, or FPGAs control platforms. To be successful at shortlisting stage, please ensure you clearly evidence in your application how you meet the essential and, where applicable, desirable criteria listed in
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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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, 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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ML/DL algorithms to hardware accelerators such as FPGAs. Conduct work on emulators for models simulating kilonovae and possibly black hole mergers in accretion disks. Basic Qualifications Bachelor's
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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