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exposure to smart cities, IoT/sensor data, SCADA, GIS, or urban water management. EVALUATION CRITERIA The selection will be based on the following criteria: Academic merit and relevance (30%): grade history
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exposure to smart cities, IoT/sensor data, SCADA, GIS, or urban water management. EVALUATION CRITERIA The selection will be based on the following criteria: Academic merit and relevance (30%): grade history
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study its impact on the degree of collaboration in hybrid teams. The successful candidate will: Develop algorithms to model team performance based on interpersonal (e.g., monitoring, communication) and
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: - Consolidated knowledge in ECC and LDPC algorithms - Consolidated knowledge in hardware description languages and hardware-prototyping toolchains (e.g., Xilinx Vivado) - Knowledge in FPGA