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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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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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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
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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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. Develop and apply state-of-the-art electron microscopy methods to study molecules-adsorbents interfaces. Collaborate closely with TUM to correlate nanoscale insights with material performance. Contribute
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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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. Responsibilities: Conduct research in time-predictable computer architecture. Designing a network-on-chip for real-time automotive systems Verify the design with modern verification methods, such as function