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process. An integral part of the project will be the development of enhanced data-driven physics methods to achieve reliable prediction of material removal rate and material removal distribution
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the project will be the development of physics enhanced data driven methods to achieve reliable prediction of residual usable life of milling tools. The approach will be validated by application to industrial
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acquisition and statistical methods is an advantage. Experience with numerical simulations (FEM). You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent
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for research and innovation in the area of food allergenicity prediction. Responsibilities and qualifications In this PhD project you will contribute to the development of innovative methods allowing
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and machine learning, you will help develop new methods for understanding complex failure mechanisms—an area where existing industrial knowledge remains limited. The project will be executed in three
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Prof. Daniel Merkle. The overall research project is based on the novel application of formalisms, algorithms, and computational methods from computer science to the design of microbial communities
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qualifications we are looking for: Excellent knowledge and practical experience on current molecular microbiology methods Experience with genomic and transcriptomics data analysis is beneficial. Experience with
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-situ spectroscopic and microscopic methods, including XRD, Raman spectroscopy, TEM, and XPS. Evaluating catalytic performance for various electrochemical reactions, such as the oxygen reduction reaction
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power electronics devices. Design methods: Develop novel design methods for power electronic converters involving advanced optimization methods Simulation: Learn advanced simulation tools such as Ansys
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the biochemical, physicochemical, and techno-functional properties of the extracted material using state-of-the-art facilities. Scale up the extraction methods using the advanced facilities available