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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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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
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mathematical control theory, and a keen interest in machine learning to apply for the PhD position within the Dynamics and Control section at the Department of Mechanical Engineering, Eindhoven University
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theory, discrete optimization and machine learning. In this PhD position you will focus on strain-aware genome assembly, variant calling and strain abundance quantification for viruses, bacteria and yeasts
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EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Would you like to work at the intersection of transportation, robotics and machine learning
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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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-on monitoring with cutting edge data-driven and physical based models, including the deployment of machine learning algorithms. The project aims to have a tangible impact on the way urban waters are monitored
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Are you passionate about hardware security, side-channel analysis and machine learning? Do you want to contribute to research within an international environment? If the answer is yes, please