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shift in the world of hardware design. On the one hand, the increasing complexity of deep-learning models demands computers faster and more powerful than ever before. On the other hand, the numerical
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Computing (Hardware for Artificial Intelligence) Reference Number: IV-151/25 Are you excited about designing hardware that mimics biological intelligence with the aim to explore and understand how the brain
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intelligence through hardware design. To this end, both the hardware foundation and the underlying hardware are continuously being developed. The design flow of these circuits and their (sub)systems is of
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Everyone is talking about artificial intelligence. But who is developing the necessary chips? We are, for example! Would you like to help drive the development of a new highly efficient AI hardware
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-driven runtime detection-based mitigation. The candidate will design techniques for automated assessment of attack surfaces and vulnerabilities across software/hardware layers.
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solutions. The successful applicant will work with mechatronic systems, including high accuracy test machinery and will be expected to develop confidence with the design and implementation of new experimental
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of embedded machine learning, neuromorphic hardware and deep learning accelerators. Want to get more information? Click here. What you will do Responsible for RTL design (VHDL, Verilog) of digital blocks and
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of ASIC/ FPGA SoC architecture and digital design Proficiency in hardware description languages such as System Verilog, Verilog, or VHDL Programming knowledge in Python and C Experience on frontend and
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hardware design, sourcing components, and assembling sensors. Laboratory and Field Testing: Evaluate sensor performance in laboratory settings and conduct field testing to validate sensor functionality. Data
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-EDX, TEM, TGA, XRD, IR spectroscopy, and BET. Prototype Development: Design and develop UV-activated gas sensor prototypes, including hardware design, sourcing components, and assembling sensors