-
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
-
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
-
, 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
-
, such as the ARM Cortex-M microcontroller for medical device applications Fluency in Matlab, Python, Embedded C, VHDL Ability to assess resource requirements and use resources effectively Understanding
-
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
-
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
-
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