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you passionate about brain-inspired AI and sustainable tech? As a PhD Candidate, you will design real-time FPGA-based systems that mimic neural processes, enabling intelligent, on-chip learning for edge
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/Electronic Engineering, Digital VLSI Design, Microelectronics, Signal Processing, or a closely related field. Technical Expertise: Proven experience in digital VLSI design, including ASIC and FPGA development
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, microelectronics (such as FPGAs and ASICs), Artificial Neural Networks, AI and Machine learning, cubesat activities, wireless technologies and all technologies related to the EEE component family (such as Si
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-level knowledge in FPGA/hardware design using hardware description languages and/or high-level synthesis (VHDL, Verilog, Vitis HLS, Vitis), and experience in programming FPGA boards. You have basic
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, microelectronics (such as FPGAs and ASICs), Artificial Neural Networks, AI and Machine learning, Cubesat activities, wireless technologies and all technologies related to the EEE component family (such as Si
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, microelectronics (such as FPGAs and ASICs), Artificial Neural Networks, AI and Machine learning, Cubesat activities, wireless technologies and all technologies related to the EEE component family (such as Si
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Qualifications and Ideal Candidate Profile: Strong academic background in computer architecture, high-performance computing, AI hardware, or processor design. Experience with hardware design languages (VHDL
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an asset. Your motivation, overall professional perspective and career goals will also be explored during the later stages of the selection process. Experience of working on a cubesat project, FPGA