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achieve automated data driven optimization (in terms of time and quality) of polishing process parameters by application of machine learning algorithms, leading to a robust, repeatable and fast polishing
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of software interfaces and data models Selection and configuration of algorithms for annotating and organizing research data and software using state-of-the-art AI technologies Ensuring the sustainability and
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aims: Develop end-to-end protocols for screening selected foods and nutraceuticals. Create advanced strategies for data integration using tailored algorithms and machine learning approaches. Demonstrate
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the context of algorithmic problems related to constraint satisfaction and graph homomorphism and isomorphism problems. It brings to bear significant new mathematical (algebraic and topological) methods
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the context of algorithmic problems related to constraint satisfaction and graph homomorphism and isomorphism problems. It brings to bear significant new mathematical (algebraic and topological) methods
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theoretical research, algorithm design, and the development of software tools that demonstrate the applicability of the new methods. Research environment The positions are hosted by the Department
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are working with a PhD student and a research fellow who have collected underwater data and preliminary algorithms. With their guidance and supervision, project aims and objectives are expected to be achieved
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immunology. For a recent list of topics that the Murrell lab works on, please see: https://scholar.google.com/citations?hl=en&user=I80vy5cAAAAJ&sortby=pubdate and https://staff.ki.se/people/benjamin-murrell
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advanced algorithms that align, merge, and aggregate datasets while maintaining data fidelity, the project contributes to the CAMS goal of enabling precise, accurate, and actionable analytical insights
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algorithms to detect complex structural variants in humans using long DNA sequencing reads. A structural variant (SV) is a large-scale alteration in the genome that involves rearranged, deleted, or inserted