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to scientific computing and other scientific domains. Candidates with strong backgrounds in stochastic analysis, numerical analysis, sampling methods, optimization, scientific computing, or related areas
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in one of the following four topics: 1) quantum algorithms for combinatorial, discrete, continuous, stochastic, robust, or multi-objective optimization; 2) hybrid quantum-classical methods, including
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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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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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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
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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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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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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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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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the potential to generate a stochastic gravitational-wave background. The work will focus on conformal extensions of the Standard Model with non-Abelian gauge symmetries consistent with neutrino oscillation data