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machine learning processing of the spectroscopic data • The optical design and development of novel custom spectroscopic sensors benefitting from freeform optics. • Integration of the in-situ
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in Mechanical or Electrical Engineering, Computer Science, or a related field. Fluency in at least one common computer programming language is required. English fluency is expected. The candidate must
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profile (rate and localization). This work focuses on machine learning assisted PSPR optimization of recently developed lean Mg-0.1Ca alloy produced by PBF-LB. After identification of the most relevant
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based models, including the deployment of machine learning algorithms. The project aims to have a tangible impact on the way urban waters are monitored, and the findings of your project will be
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shifts in cell state and cell fate. Integrate spatial transcriptomics data to anchor these predictions in tissue context. Develop machine learning methods (e.g. graph neural networks, variational
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machine learning solutions to optimize the component lifecycle directly contributing to a more circular economy. Information In the manufacturing landscape, determining whether a component should be
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, computer science, electrical engineering, applied mathematics, machine learning, or in a similar field, or have completed at least 240 credits in higher education, with at least 60 credits at Master’s level
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, including Python and Git/Github; data analysis experience, including AI and machine learning methods; and a broad knowledge of college sports and/or elite-level competitive athletics. Special Instructions
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and image processing. Prior experience with data fusion, machine learning, or super-resolution methods will be considered an asset. Candidates should be motivated to conduct independent scientific
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at TU Delft. In this project we also work together with experimental groups at TU Delft and beyond. The Delft Bioinformatics Lab has strong algorithmic and machine learning expertise, with a profound