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Future-Proof Smart Logistics. It aims to contribute to the realisation of the PI concept by developing advanced machine learning-based decentralised decision-making algorithms. These algorithms will enable
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-based, probabilistic, and in-memory computing, are based on a wide variety of physical processes, materials, architectures, and algorithms. For effective implementation, these aspects need to be mapped
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studies independently, working with diverse datasets, developing algorithms and decision rules, and contributing to the refinement of data-driven intervention strategies. Tasks As a postdoctoral researcher
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their PhD before January 2026 are also encouraged to apply. Applicants should demonstrate proficiency in programming languages and tools commonly used in computer science. Hands-on experience with genetic
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on the resulting algorithms and pipelines. As an emerging paradigm, differentiable programming builds upon several areas of computer science and applied mathematics, including automatic differentiation, graphical
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for the participation in multiple international conferences and interaction on site with project partners. Your qualities The ideal candidate: holds a PhD in a topic related to energy science, geoinformatics
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qualities You hold either PhD in deep learning techniques and an interest in climate science, or a PhD in Meteorology or Climate Science having clear experience with deep learning techniques. You possess
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to build predictive models Collaborate closely with experimentalists and modelling experts Project Environment This position is part of a collaborative research project involving: Two PhD students at TU
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reviews. This postdoctoral position offers a unique chance to research and improve teaching quality and teacher development. You will further develop, test, and refine RIDE across multiple faculties
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contribution to marine conservation strategies. You will join a small but diverse and multidisciplinary team of research assistants, PhD candidates, residents (specialists-in-training), veterinarians