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Field
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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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and to accelerator design. This is expected to be a supplementary element with a primary focus on hardware system development. Proposals should also highlight their contribution to priority areas
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with plasma-source, plasma-lens and/or dipole-magnet design and hardware. Experience with numerical simulation of plasma accelerators (particle-in-cell simulations) and/or particle tracking. Programming
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, along with documentation - Conduct analysis and assist staff in conducting analysis - Maintain and administer hardware solutions for project and group compute tasks - Advise Division and
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. Demonstrated ability to integrate hardware, software, and analytics into coherent experimental platforms. Additional information: Application date: Priority consideration will be given to candidates who apply by
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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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, or thermal systems. Demonstrated ability to integrate hardware, software, and analytics into coherent experimental platforms. Additional information: Application date: Priority consideration will be given
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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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projects or public-private-partnership initiatives with strict timelines and defined goals. Demonstrated ability to supervise postgraduate students. Experience in High Performance Computing hardware
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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