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filled The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application
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synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application to the analysis of time series. In particular, the project
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optimization models and algorithms to address the above questions. Given the uncertainties involved in food supply chains, we prefer candidates who have a background in (stochastic) optimization methods (e.g
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breakage models, e.g. with stochastic tessellations Development and implementation of estimation methods for the model parameters, e.g. with machine learning or statistical methods Lab work and collection
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be applying methods such as sensitivity analysis, robust optimization, and stochastic modelling as you work on your project. You will be seconded with the Chalmers University of Technology (Sweden) and
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optimization, and stochastic modelling as you work on your project. You will be seconded with the Chalmers University of Technology (Sweden) and Mærsk McKinney Møller Center for Zero Carbon Shipping (Denmark
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and machine learning to tackle the complexity of force allocation and motion planning under uncertainty and actuator failures. The project combines theoretical research in stochastic optimal control
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position for candidates interested in interpretable AI, stochastic optimal control, deep learning and high-impact research in sustainable mobility. About us The position is located at the Systems and Control
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confocal, super-resolution stochastic optical reconstruction microscopy (STORM)Excellent verbal and written communication skills in English with proven ability to prepare research manuscripts and
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Artificial Intelligence, Stochastic Modeling and Optimization, and Data-driven Decision-making with applications in supply chain and operations management. This position offers an exciting opportunity