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computing, and digital twin technologies for mission autonomy and predictive analytics. The research assistant will develop real-time flight control algorithms, AI- based data processing, and mission
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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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analytics. The research assistant will develop real-time flight control algorithms, AI- based data processing, and mission management platforms, contributing to the deployment of smart UAV demonstrators under
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computing, and digital twin technologies for mission autonomy and predictive analytics. The research assistant will develop real-time flight control algorithms, AI- based data processing, and mission
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join our research team focused on the development of innovative and sustainable sensor systems for industrial applications. This position offers an exciting opportunity to contribute to cutting-edge
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close-to-field conditions, and (ii) a fully autonomous phenotyping robot, Phenomobile.v2+, equipped with a set of sensors (LiDAR, RGB, IR, and Spectrometer) that enable advanced plant measurements
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. We propose the integration of randomized algorithms into sparse optimization frameworks for the purpose of completing multidimensional networks by studying the theoretical foundations behind randomized
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. The candidate will need to master sensor technologies, embedded systems, and communication protocols, while integrating real-time data into cloud platforms to develop reliable, secure, and scalable solutions
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to join our cutting-edge team, working on the development of advanced AI/ML algorithms for battery management systems (BMS) in electric mobility and micro mobility applications. The primary focus will be
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environmental sensors and thermal cameras (UAV/IR). Comparative analysis of PV configurations: with vs. without vegetation. Studying dust deposition and its impact on energy performance. Analyzing meteorological