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Field
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learning algorithms. We combine statistical methods with online reinforcement learning algorithms to develop reinforcement learning algorithms and inferential tools. The successful applicant will be expected
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part of an interdisciplinary research team dedicated to advancing management science, the fellows will develop novel quantitative methods at the interface of statistical learning, experimental design
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approached by tools in both computational chemistry and applied mathematics. Specifically, the project involves the development of mathematical modeling strategies at both numerical and analytical level to
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uncertainty, and explainability and reliability. The project will have an applied focus, so the aim is to develop algorithms and implement these in systems that enable testing and evaluation in collaboration
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visualisation (libraries such as Three.js, OpenGL, VTK, or similar); - Advanced knowledge of optimisation algorithms; - Previous experience with software development for logistics problems; - In-depth experience
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. The position is part of a small team that works on the development and optimization of algorithms for these problems, as well as proofs on theoretical complexity bounds. Common tasks include: Developing ideas
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distributed systems. Minimum requirements: - Solid knowledge of database engine implementation; - Solid knowledge of optimization algorithms (Volcano/Cascades); - Practical development experience with
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learning and mechanistic modelling, sensor network development, efficient data sorting and processing algorithms, real-time and model predictive control, and transformative applications in the wastewater
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; T2: State-of-the-art review and model testing; T3: Development of algorithms for information extraction; T4: Algorithm testing and validation; T5: Preparation of reports, presentations, and other
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the development of fast and scalable algorithms for many-component systems, and of coarse-grained models that can be analyzed and simulated. Strong applicants with backgrounds in applied and computational