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complex nonlinear models, with unique research opportunities in algorithm and hardware co-design, particularly for resource-constrained environments. Applications focus on edge intelligence
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neural network types for efficient hardware implementation Developing an FPGA-optimized design Performing device-aware training using both simulated and measured datasets Validation of the complete
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for dedicated implementations of latent reasoning. These investigations will be grounded in hardware through co-design of neuromorphic hardware to improve accuracy, latency, data efficiency, and energy
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that you meet the following essential criteria: Have a relevant MSc in Electrical and Electronic Engineering/Computer Science (or related discipline). Good hardware / software design skills. Good
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RF circuits in Python Validate the developed models against circuit simulations and measurements Perform hardware-algorithm co-design for AI-driven 6G transmitters Publish and present your results at
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models of analog and RF circuits in Python Validate the developed models against circuit simulations and measurements Perform hardware-algorithm co-design for AI-driven 6G transmitters Publish and
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others of the following tasks: Investigate and design optimal computing and communication architectures for hardware acceleration of large-scale machine learning workloads Perform characterization
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environmental monitoring, search and rescue, and early-warning services. The work spans protocol design, embedded firmware on STM32WL hardware, and large-scale evaluation through network simulation, and it
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, researchers and industry. Read more: https://wasp-sweden.org/graduate-school/ Project description The project focuses on the design and analysis of modes of operation for symmetric-key cryptography
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ASIC design and/or SDR platforms(e.g., Zynq SoC, RFSoC,) Knowledge of fixed-point design and hardware constraints Strong programming skills (C/C++, Linux environments) When interested in the higher