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Description A PhD position starting November 1, 2025 (with some flexibility in both directions) is available at the University of Southern Denmark (SDU) for research in an exciting project in algorithmic
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A PhD position starting November 1, 2025 (with some flexibility in both directions) is available at the University of Southern Denmark (SDU) for research in an exciting project in algorithmic
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algorithms. The targeted starting date is 1 September 2025,or as soon as possible thereafter. Project description This project will explore the algorithms, advantages, and applications of quantum computing
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algorithmic aspects of cheminformatics. The position is founded by the Challenge Programme of the Novo Nordisk Foundation: “Mathematical Modelling for Microbial Community Induced Metabolic Diseases ”, led by
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, Responsibilities and qualifications Electricity markets are undergoing a rapid transformation: Market participants are deploying AI algorithms towards making their bidding decisions. AI algorithms are instructed
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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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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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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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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