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Applicants should hold a relevant MSc degree in electronics, electrical engineering, computer engineering, or related fields. Required Qualification: Solid background in digital CMOS design and deep learning
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-of-the-art facilities and deep expertise. Our infrastructure spans the full spectrum from fundamental electrochemical studies to the fabrication of lab-scale components and devices. Responsibilities and
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background in CMOS/VLSI design, computer architectures (preferred RISC-V architecture), and deep learning principles. Experience with industry-standard EDA tools such as Cadence suite: Genus, Virtuoso, Spectre
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system designed to safeguard underwater infrastructure. This is a unique opportunity to dive deep into advanced robotics research, gain hands-on experience with real-world testing, and make your mark in a
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, preferably Reinforcement Learning (e.g., Q-learning, Deep Q-Networks) or other control algorithms. Proficiency in Python, MATLAB, or similar for data analysis, modeling, or AI implementation. Strong written
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on the way in which the proposal should be structured.) Proposals should incorporate an assessment of relevant quantitative methodologies. To learn more about the research and teaching profile