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characterization of various coupled silicon nitride nanoelectromechanical oscillators and measurement of reservoir computing based on programmed FPGA scheme, (ii) development of multi-physics models in both
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Università degli Studi di Roma Tor Vergata - Dipartimento di Ingegneria Elettronica | Italy | 3 months ago
activity will focus on the development of an FPGA-based digital beamforming processing system for real-time radar, sonar or MIMO communication applications. The work will include the definition
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out research on applications of FPGA in optical communications and sensing. About the role: Post is a part of a newly funded collaborative project, FOCUS, between Aston University (with Prof. Andrew
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. Being able to identify good Masters of Engineering projects and to partner with industry or outside laboratories on potential interactive projects is desirable. Please see our website, https
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digital circuits. Experience with various techniques for high frequency characterization, e.g., lockin amplifier, oscilloscope, FPGA, and so on. Mandatory experience in programming (e.g. python or matlab
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. Where to apply Website https://aplicaciones.uc3m.es/atenea/publico/1/listarConvocatorias Requirements Research FieldEngineering » Communication engineeringEducation LevelBachelor Degree or equivalent
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communication systems, advanced electronic instrumentation, electronic circuit design, FPGA programming and sophisticated signal-processing algorithms for space-based synchronisation applications. This role will
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website: https://www.esa.int/ Field(s) of activity/research for the traineeship Main objectives of the Research Fellowship Cognitive cloud computing in space (3CS) is emerging as a key paradigm for future
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well as an on-board processing system capable of target recognition and classification (people, traffic signs, etc.).The research program requires expertise in digital electronics applied to the configuration of FPGA
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, or approximate computing. Knowledge of hardware security, fault tolerance, dependable computing, or hardware reliability. Experience with GPU, FPGA, embedded, or specialized AI accelerator platforms. Experience