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Field
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point of this PhD to link experimental work to enzyme function and kinetics. You should have a solid foundation in chemistry and biotechnology, as well as both theoretical knowledge and hands
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and image data processing. Specific knowledge related to neural network design, training, and optimization is required. You will be joining a group with core expertise in sensor data analytics from
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particular, we will use topology and shape optimisation methods to compute the optimal domain shapes that can stabilise solutions with desired/prescribed properties. We will use methods from inverse problems
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opportunities within the university No Academic admission requirements "Good" or better MSc, Dipl-Ing, or equivalent "Diplom" as accepted by the University of Stuttgart and a profound knowledge of the fields
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or BSc + one year of Master Studies in mathematical engineering, mathematics, computer science, electrical engineering or similar. Solid mathematical and analytical skills, including optimization and/or
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Analytics Software Engineering for Distributed Systems Telematics Theoretical Computer Science Optimal Transport Work on a scientific topic aims to enable the student to understand complex scientific work
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experiments, including occasional wet lab work Merging experimental results, simulations, and literature for optimal conditions inducing movement differences, in collaboration with the team. Participation in
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such as healthcare, finance, cy- bersecurity, and autonomous vehicles, where they interact dynamically with external knowledge sources, retain memory across sessions, and autonomously generate responses and
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microbiomes to optimize end-of-life material processing and circular resource recovery. This position will be part of the UT-ORII’s Circular Bioeconomy Systems (CBS) Convergent Research Initiative (CRI
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, mathematical engineering, mathematics, or similar; - Solid mathematical and analytical skills, including mathematical optimization and information theory; - Solid knowledge and skills in machine learning