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Field
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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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with designs for infrared or optical image sensors, sensor characterization, performance optimization, and calibration methods. Digital logic design and implementation using FPGAs. Experience in DSP
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that integrate analog/digital architectures with neuroscience-inspired algorithms Implement and optimize biologically plausible learning rules on FPGA and high-performance computing (HPC) platforms Basic
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optimization, and calibration methods. Digital logic design and implementation using FPGAs. Experience in DSP programming and interfacing. Programming in C++, UNIX, Python, Matlab, and familiarity with image
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systems including bare-metal embedded systems, RTOSes, FPGAs, and embedded Linux. You will have opportunities to develop tools, techniques, and processes to solve some of the most difficult software
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intelligence. This PhD project will leverage the power of field-programmable gate arrays (FPGA) to deploy machine learning models on the edge with low latency and high energy efficiency. This added intelligence
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should also be able program, at a basic level, hardware which utilises FPGAs and small embedded processors; these devices are increasingly being used for teaching/research within Electronic Engineering
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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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individual with the ability to support dynamic research activities and ensure smooth technical operations. You should apply if Specialist in server, storage, network, and FPGA setup/support. Expert in IT
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experience or equivalent. • Knowledge with building embedded control systems in Python or C++/C#. • Experience in machine vision and/or industrial automation. • Experience with PCB design, DAQ, and FPGA