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Field
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hardware avanzadas basadas en FPGA. Intregación hardware/software utilizando C/C++, Python. Despliegue de sistemas empotrados utilizando Linux. Implementation of advanced FPGA-based hardware solutions
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topics. Candidates should have some experience working with FPGAs as well as an understanding of computer networks. Experience with both RTL and HLS design is favoured. The ideal candidate would have some
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for control system interfaces. As part of a team, develop FPGA-based control systems to support AQT experimental operations. Support gateware development for FPGA-based systems, ensuring proper integration and
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inteligencia artificial/machine learning. Tecnologías IoT y sistemas distribuidos. Conocimientos en FPGA. // Knowledge of artificial intelligence/machine learning, oT technologies and distributed systems, and
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. During the project, these modules will be emulated by RF FPGA boards designed at the beginning of the project. The first objective of the project will therefore be to propose a very low cost wireless link
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LevelUndergraduate Skills/Qualifications - Conocimientos de programación y uso de sistemas basados en microprocesadores. - Conocimientos de diseño de circuitos digitales para sistemas reconfigurables basados en FPGA
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skills in designing advanced neural networks (including Vision Transformers, Graph Convolutional Networks, and Spiking Neural Networks), hardware implementation of algorithms for reconfigurable FPGA
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monitoring will also be integrated into this PhD. Thermal prediction models are currently implemented on a Field Programmable Gate Array (FPGA), while the thermal PI controller (which will be further developed
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 7 days ago
for a LIBS spectrometer: laser control; carriage and galvanic mirrors control; spectrometer development; power supply. The spectrometer module will feature a STM microcontroller and a FPGA with a USB-3
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a suit of fast electronics (FPGA) that will allow the following achievements: 1) To rapidly repeat single-particle experimental dynamical trajectories to gain sufficient counting statistics in order