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classification for hyperspectral and fluorescence lifetime datasets. Optimize algorithms for batch processing and scalability, enabling high-throughput, automated analysis of large image datasets from fluorescence
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disease into specific subclasses. You will develop AI algorithms to train models that predict if individuals (from which we create circuits) are prone to develop disease and to identify conditions that have
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top 10 IT security/crypto conferences) Strong mathematical and algorithmic CS background, economics/finance - a plus Good skills in programming and scripting languages Commitment, team working and a
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/non-convex optimization (Mandatory) Signal processing Computational electromagnetics focused on time domain algorithms (Mandatory) Programming skills in MATLAB (Mandatory) Good oral presentation skills
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Mobility (UAM) Experimental emulators, prototypes, testbeds and distributed research infrastructure test platforms Set up real-world testing both in the laboratory using emulators and software defined radios