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1-D and 2-D modelling approaches fail to capture the complex three-dimensional effects critical to high-frequency, high-efficiency operation in modern converters. The PhD candidate will: 1
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Optimization techniques (e.g. gradient-based, stochastic, linear programming) Machine learning techniques Energy processes and systems Furthermore, a successful candidate has: Excellent use
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Cancer Consortium (DKTK). For the DKTK partner site Munich, we are seeking for the next possible date a PhD Student in Mutational Processes Driving Somatic Evolution Reference number: 2025-0224 From
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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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, geometallurgy or related field Experience in either stochastics, deep learning or minerals processing is needed Structured and solution-oriented working style, analytical thinking and above-average committment
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dynamic community of PhD students and actively supports diversity. We are looking for a motivated applicant with good competences in operation research who wants to gain hands-on experience in cutting-edge
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diversity. We are looking for a motivated applicant with good competences in operation research who wants to gain hands-on experience in cutting-edge energy modeling and analysis to be part of the Ports as
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high-quality research on interpretable and learning-based stochastic optimal control for over-actuated electric vehicles, with a focus on ensuring robustness and fail-safe operation. You will: - Develop
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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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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