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Field
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. Development of real-time optimization algorithms and model predictive control (MPC) strategies for adaptive process management. Addressing data sparsity and data quality issues in industrial process data
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of simulated sensors (vision, sonar, LiDAR, inertial navigation). - Implementation of guidance, control, and planning algorithms under environmental constraints. - Implementation of distributed simulation
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department in the ISSA expertise center that develops advanced AI solutions involving AI models, algorithms, implementations, sensors and hardware for small scale edge up to large scale distributed and hybrid
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scheduling algorithms to ensure operation continuity and reliable information exchange among batteryless sensors. Where to apply Website https://lavoraconnoi.unitn.it/research-assignments/dipartimento-disi
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change, climate change attitudes, and risk under environmental heat stress by combining psychological profiling, biological lab data, physiological time series, and sensor data. The postdoc will play a
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. Specifically, our Responsive Sensing and Analyticsteam is developing efficient and scalable algorithms to provide emergency services with the necessary safety-relevant information and analysis results based
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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field testing and performance analysis Interface with sensors and develop algorithms to process and analyze dynamic data Work directly with industry sponsors to define requirements, scope, and project
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will be responsible for working, as part of a skilled technical team, to write and test software in high level languages, and to develop and test signal processing algorithms from concept