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candidate will develop and test novel user interfaces that integrate state-of-the-art Large Language Models (LLMs) with novel logic-based multi-robot planning algorithms. This work will be evaluated through
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will take advanced courses to build and deepen your skills, implement and evaluate algorithms, and develop your ability to write and present scientific work. We are a supportive team that will welcome
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on preferences the candidates will work along one (or more) of the following different directions: theoretical foundation involving quantitative models (e.g. stochastic, timed weighted, hybrid automata) and logics
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available sensor and meter infrastructure, affordable computational resources, and advanced modeling algorithms. MPCs excel in handling constrained optimizations and new operational conditions, whereas RLs
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for Science & Technology (KAIST), and an external stay at KAIST will be included as part of the PhD program. Qualifications Proficiency with Python Experience implementing various Machine Learning algorithms
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research aimed at enhancing the efficiency and performance of PRO systems for sustainable energy solutions. PRO process is dealing with harvesting clean energy from the salinity gradient between different
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case is central to this position. SimMobility is based on activity-based mobility modelling theory, simulating agent-level behavior such as route, departure-time, and mode choice within an activity-based
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the computational activities in a large closed-loop collaboration that includes computational, biotechnological and automation activities, requiring a solid understanding of the different areas involved in
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that integrate state-of-the-art Large Language Models (LLMs) with novel logic-based multi-robot planning algorithms. This work will be evaluated through simulations and with physical drones in mock search-and
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experimentation with Asst. Prof. Eli N. Weinstein. Your goal will be to develop fundamental algorithmic techniques to overcome critical bottlenecks on data scale and quality, enabling scientists to gather vastly