-
into the co-design of ultra-low-power AI hardware architectures tailored for edge computing applications. The research aims to develop neuromorphic processors, FPGA/ASIC-based AI accelerators, and intelligent
-
parallel processing, FPGA coding and analysis, along with Machine Learning and AI based image analysis. The final aim of the project will be to generate in-situ / live film profile data to coating line
-
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
-
manipulation of ultracold gases. Examples include basic electronics, development of FPGA devices, development of narrow-linewidth lasers, laser frequency stabilisation and control, image analysis, data
-
/or FPGAs) E3 Experience in hardware assembly and fabrication, with experience in PCB manufacturing/ assembly, prototyping, and willingness to acquire new skills, e.g. in additive manufacturing and chip
-
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