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Field
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to allow remote configuration via PYVISA - Use the appropriate hardware setup and a PYVISA script to perform typical characterization tests for analog and mixed circuits - Create a tutorial to disseminate
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algorithms for parallel/distributed AI/ML Hardware-aware and resource-efficient partitioning for parallel/distributed AI/ML Optimization of process-to-process communication in parallel/distributed AI/ML
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electronics for sensor readout systems, including ASIC and SoC design and verification. Strong experience in hardware–firmware co-design for data acquisition and processing pipelines, including FPGA-based
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neuromorphic hardware in particular Integrated Circuits along with a strong publication record. Position level is commensurate on experience, and we are actively encouraging applications to both levels
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for drone swarms. The role will focus on multi-agent visual perception techniques. Group website: https://personal.ntu.edu.sg/wptay/ Key Responsibilities: Develop signal processing and machine learning
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and a large magnetically shielded room equipped with advanced motion-tracking systems. The role involves developing and applying innovative hardware and software solutions, contributing to the planning
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) Ideal candidates have Specialist Knowledge in Neuromorphic Engineering, with experience in designing and testing Mixed Mode IC systems, working with and/or developing neuromorphic hardware in particular
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Hardware Implementation”. This project is funding by the EU funding schema CHIST-ERA and comprises a European consortium. This consortium is led by the Centre for Secure Information Technologies (CSIT: https
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hardware on FPGAs along with a strong publication record. Position level is commensurate on experience, and we are actively encouraging applications to both levels. Applicants should also present a clear
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hardware prototyping to evaluate approaches against performance metrics such as energy efficiency, inference accuracy and computational latency. The successful candidate will join the Nanoelectronics