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Bruno Iñiguez

Systems Engineer at Spectrum Effect

Bruno Iñiguez has a diverse range of experience in the telecommunications industry, primarily working with Huawei Technologies. Bruno started their career as an RF Engineer at Huawei Technologies, where they focused on data analysis and network optimization for UMTS projects. Bruno later joined COTAS Ltda. as an RF Engineer, where they analyzed drive test data and provided tuning suggestions. Bruno then worked at Millicom as an RF Engineer, where they were involved in various projects, including UMTS, GSM, and swap projects. Bruno performed cell planning, simulation, and provided initial tuning suggestions. Bruno also worked as an RF Consultant at Nextel de México, where they worked on a 3G project, and at corporacion nacional de telecomunicaciones, where they contributed to a 3G and LTE project. Bruno's experience also includes working as an RF Consultant for América Móvil Vendor and AT&T - Huawei Technologies. In their most recent role as a Systems Engineer at Spectrum Effect, they performed system analysis, design, and characterization for LTE, LTE-Advanced, and 5G NR networks. Overall, Bruno Iñiguez has extensive experience in system analysis, network optimization, and project management in the telecommunications industry.

Bruno Iñiguez obtained a Bachelor's Degree in Telecommunications Engineering from Universidad Católica Boliviana, where they studied from 2002 to 2007. Bruno then pursued a Master's Degree in European Master of Research on Information and Communication Technologies (MERIT) from Universitat Politècnica de Catalunya, completing their studies from 2010 to 2012. In 2008, Bruno enrolled in a Postgraduate Course in Higher Education at Universidad del Valle (BO), but the end year is not specified.

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São Paulo, Brazil

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Spectrum Effect

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Spectrum Effect is a provider of spectrum analytics services designed to automatically detect the impact of external and unintended internal RF interference. The company's spectrum analytics incorporates both traditional and deep machine learning (ML) capabilities to detect and classify interference within operator networks enabling ML modelsto be trained using operator network data, as well as the deployment of pre-trained ML models and operators across the globe to protect their sizable investment in licensed spectrum.The company was founded in 2015 and is headquartered in Kirkland, Washington.


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51-200

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