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(UQ) for machine learning and its validation. Your areas of research will be chosen based on both your own expert judgement and insight into trends and developments and on team requirements to ensure
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required in the organization of the logistics operations of educational processes to achieve a high quality situation where any learner at any time based on their own learning speed, level and ambition can
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develop a simplified model focusing on the leader stage. You will: Analyze experimental data and microscopic simulations Identify relevant physical features and parameters Apply machine learning techniques
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Vacancies Academic staff Support staff UT Student Jobs UT as employer UT as employer Employment conditions Career and development Pre and onboarding HR Excellence in Research Tenure Track PhD EngD
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in combination with other machine learning techniques, to create predictive models. You will engage in an interactive feedback loop with domain experts to analyze discovered models and remove any
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postdoctoral researcher, you will use various techniques to investigate fiber-reinforced structures in nature (i.e., fungi), including additive manufacturing, computer simulations, materials testing, and
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from resources like Wikidata or fan wikis. In addition, both the full text and the existing triples can be leveraged for the extension of the knowledge graph via automated reasoning, inferential learning
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societies bring about technological change, and how technological innovations change society. With ~25 tenured staff and a large group of postdocs and PhD candidates, researchers in the TIS group try to
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towards a future-proof logistics system with a special focus on machine learning-based collaborative scheduling, resource sharing, and self-organisation. The EngD position corresponds to a 2-year post
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assessment. You will be provided with access to various engineering and computation toolsets along with the high-performance computer. A good background in numerical methods and computational platforms is