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teaching in areas such as AI in Business Analytics, Generative AI and Business Decision-Making, Optimization, Predictive Analytics, Stochastic Processes, Data Mining, and Data Visualization. Teaching will be
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reinforcement learning (RL), active learning, Bayesian decision theory, and stochastic optimisation for partially observed and evolving systems. Key research directions include: adaptive data acquisition
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on stochastic/robust distributed optimization methods, accounting for uncertainty in weather conditions, demand forecasts, and hydrological inflows. Distributed methods are motivated both by the complexity
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of Formal Methods/Reinforcement Learning, such as AAAI, AAMAS, IJCAI, NeurIPS, ICML, ICLR etc. We expect excellent skills in mathematics, especially knowledge in formal methods, stochastic systems and
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Stochastics. The positions have 1st January 2027 as earliest possible start dates. You can find more information about the postdoc positions available in the areas listed here: https://math.au.dk/en/about
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at the intersection of stochastic processes, probability theory, and machine learning. The PhD candidate will investigate continuous-time Markov chains (CTMCs) and jump-diffusion processes for generative tasks. A core
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, such as multi-objective optimization, model predictive control, mixed-integer optimization, stochastic optimization, energy management, or production scheduling. Good knowledge of integrated energy
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include but are not limited to statistics and probability, stochastic modeling, scientific computing, mathematical optimization and control, numerical analysis, and relevant adjacent fields. Candidates with
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environmental, climate and economic domains. It applies economics to these challenges, formulating problems in a context of uncertainty, making the most of weak data and signals, and applying dynamic, stochastic
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for stochastic, distributionally robust, and mixed-integer nonlinear optimization problems. The successful candidate will conduct research at the intersection of stochastic programming, optimization under decision