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and Data Analytics in Air Traffic Management Systems. The selected candidate will work on developing innovative optimization and Machine Learning models to address key challenges in the future airspace
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. The role will focus on developing machine learning and mathematical optimization solutions for electric vehicle fleet charging optimization under different constraints. Key Responsibilities: Formulate
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-tier AI conference and journals. Internal knowledge transfer, and collaborative research efforts Requirements: Minimally a Bachelor’s degree in Artificial Intelligence, Computer Science, Optimization
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, including traffic prediction, optimization, and safety enhancements. Key Responsibilities: Develop and implement machine learning models for air traffic prediction, flow management, and conflict detection
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Responsibilities Development of new machine learning modeling approaches Development of new advanced control and optimization algorithms Optimization of carbon capture process operation Provide regular project
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. The Variational Quantum Eigensolver (VQE) is one such optimization algorithm that helps determine the total energy of molecules. In the NISQ era, simulating molecules with more than 16 qubits is a hard problem
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in a research project on machine learning modeling and optimization of vertical farms. Key Responsibilities: Development of new machine learning modelling approaches Development of new advanced control
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and dynamic models. Development of optimization algorithms for the unique operational characteristics of the onshore microgrids. Development energy management system including frontend and backend. Job
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writing/presentation Job Requirements PhD degree in an engineering field related to this project Experience in dynamic modeling, machine learning and optimization & controls Having basic knowledge in carbon
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and units within the university. Its primary goal is to streamline and optimize processes, enhance operational efficiency, and deliver high-quality services to support NTU's core mission of education