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neuromorphic mixed-signal/near-analog circuits for next generation edge-AI systems. You will gain skills in custom chip design, artificial neural networks and edge-AI system implementation. The work combines
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impactful, application-driven research. This position supports a strategic research project on full electric harbour craft microgrid modelling, onboard energy management systems (EMS), and hardware-in
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project team at INESC-ID is working on the development of specific components for the OpenSSD-V platform, focusing on hardware/software co-design and circuit synthesis for ASIC/FPGA. The objective
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approaches in radiation therapy. The second position will be offered to a candidate with a desire to implement deep learning algorithms on clinical hardware (either developing new imaging hardware
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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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and Trust (SnT) at the University of Luxembourg is a leading international research and innovation centre in secure, reliable and trustworthy ICT systems and services. We play an instrumental role in
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. The second position will be offered to a candidate with a desire to implement deep learning algorithms on clinical hardware (either developing new imaging hardware or implementing deep learning approaches
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Engenharia de Sistemas e Computadores: Investigação e Desenvolvimento em Lisboa” is a Research and Development and Innovation Organization (R&D+i) in the fields of Computer Science and Electrical and Computer
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Systems (Mixed-Signal IC Design) Ideal candidates have Specialist Knowledge in Neuromorphic Engineering, with experience in designing and testing Mixed Mode IC systems, working with and/or developing
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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