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received after the review date will only be considered if the position has not yet been filled. Position description The Computational Medicine Research Group led by Prof. Pratik Shah at the University
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/ The underlying project is under supervision of Prof. Dr. Peter Zaspel and Prof. Dr. Michael Günther. The team of Prof. Peter Zaspel is located at Bergische Universität Wuppertal. The international team focuses
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close cooperation/mentoring with Prof. Dr. Herman Jungkunst and other institutions across Europe, e.g., Ghent University, Belgium or Agroscope, Switzerland. The NitroScope project aims to develop systemic
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-Landau in close cooperation/mentoring with Prof. Dr. Herman Jungkunst and other institutions across Europe, e.g., Ghent University, Belgium or Agroscope, Switzerland. The NitroScope project aims to develop
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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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climate experts with the aim to address existing uncertainties about climate feedbacks at the boundaries between oceans, land, ice, and atmosphere. Our interdisciplinary approach and state-of-the-art
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responsibility is to conduct high-quality research on hybrid artificial intelligence. You will: Combine deep learning to capture long-term patterns and uncertainties with stochastic model predictive control
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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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. The military supply logistics network faces uncertainty, which is dependent on the adversary and their means. The military supply logistics networks need to consider different scenarios, requiring the that it
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regulations and product certification due to the inherent uncertainty of how AI systems make decisions. Classical engineering development guidelines, are difficult to interpret or simply not transferrable to AI