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machine learning tools for the efficient analysis of the experimental data. For more information, visit our web page www.soft-matter.uni-tuebingen.de We are looking for a motivated PhD student to contribute
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are being developed that provide AI-supported tools to identify suitable sources and optimize utilization decisions throughout the product life cycle. Various machine learning approaches are to be used
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These are positions for Doctoral Students, based in Tübingen in an interdisciplinary research group working at the interface of Machine Learning, Medicine, and Biology. Doctoral Students will engage
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Society. The Department of Machine Learning and Systems Biology led by Prof. Dr. Karsten Borgwardt is looking to recruit a highly-motivated PhD student (m/f/d) for the project Building Clinical Foundation
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Degree PhD Learning Sciences (alternatively Dr phil, Dr rer nat, Dr med; not recommended for international students) Course location München Teaching language English Languages Courses are held
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on machine learning assisted PSPR optimization of recently developed lean Mg-0.1Ca alloy produced by PBF-LB. After identification of the most relevant parameters adopting a design of experiments
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skills (Python, R, Java, …) and interest to work in polyglot software environments Practical experience with machine learning and AI methods and an interest to learn, adapt and apply ML methods
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, the details of the process are not yet fully understood. Mechanistic learning, the combination of mathematical mechanistic modelling and machine learning, enables a data-driven investigation of the processes
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environments Practical experience with machine learning and AI methods and an interest to learn, adapt and apply ML methods to challenging problems in mass spectrometry. Independent and cooperative working in
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assessment of chemical plants using HAZOP analysis Use of process modeling and simulation to enhance quantitative assessments Use of machine learning to support HAZOP discussions with the aim of obtaining a