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of the central challenges on the path toward large-scale quantum computing. In this PhD project, you will investigate how machine learning can enable faster, more scalable QEC decoding. The goal is to develop new
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, multi-omics data integration using machine learning, and potential collaborations with clinical and translational researchers. The project is well-suited for candidates with a background in bioinformatics
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systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in working with nuclear fuel modelling, machine learning
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opportunities and diversity as a strength and an asset. Description of the workplace The research group for associative learning conducts research in neurophysiology and neuroscience, with a particular focus on
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Join us in designing stable materials for sustainable energy devices with machine-learning-accelerated simulation and modeling. Work assignments The postdoctoral researcher will develop machine
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biostatistics group). The department is situated at campus Solna. Further information can be found at http://ki.se/en/meb The KI Psychiatric Genomics Institute (KI-PGI) at MEB is seeking 1-2 postdoctoral