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mechanical fabrication; • embedded systems and sensor integration; • ROS, C++, or Python programming. • Candidates should demonstrate strong practical problem-solving skills
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. • Familiarity with sensors, building automation systems, building communication protocols (e.g., BACnet, Modbus, KNX), and IoT-based data acquisition.Basic knowledge of machine learning and building controls
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will work closely with interdisciplinary researchers and engineers to develop novel AI algorithms, publish in top-tier venues, and translate research innovations into real-world sustainability solutions
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implementation novel machine learning and large language model (LLM) algorithms for Green AI applications. Support the research of efficient AI techniques, including model optimization, parameter-efficient fine
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localization algorithms, spearhead energy-aware navigation planning systems development, and oversee real world validation of underwater navigation technologies while mentoring junior team members. Key
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of optimization and generalization to inform the design of practical training algorithms. To produce high-quality publications in top-tier machine learning conferences and journals. To offer guidance and assistance
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learning-based model predictive control (MPC) algorithms for multi-agent multirotor drone navigation around vessels in maritime environments. The role will focus on integrating multirotor crash predictions
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, and operation of experimental test rigs, including laboratory setups and field experiments. Key Responsibilities: Experiment setup and testing Component assembly Sensors and hardware integration Data
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to urban heat island effects, microclimate variation, and platform and sensor performance. · Assist in preparing figures, tables, maps, technical summaries, and presentation materials for project meetings
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This position focuses on advancing the mathematical and computational foundations of interpretable algorithms for dynamical systems and explainable AI. The successful candidate will develop new