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interdisciplinary approach encourages contributions to related projects, including applications of machine learning to autoimmune disease and non-invasive diagnostics using cell-free nucleic acids. Duties Develop and
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). Meritorious: It is also an advantage if you have experience with: Machine learning. Coupling algorithms of fluid-structure interaction solvers. Computational aeroacoustics. Swedish is not required
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advising on methods and systems, assessing quality and properties of data, assisting with resource allocation proposals, machine learning workflows, dataset curation, organization, and sharing, data
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of computational fluid dynamics (CFD). Knowledge of finite element method (FEM). Meritorious: It is also an advantage if you have experience with: Machine learning. Coupling algorithms of fluid-structure interaction
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on processing and analyzing large-scale proteomics datasets. The Project Assistant will develop workstreams and execute existing omics and machine learning-based pipelines to process and postprocess this data
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interest in Artificial Intelligence and Machine Learning development, Proficiency in written and oral communication in English. Place of employment: Karlskrona. Employment level: 100%. Commencement: To be
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software development. Documented experience or interest in Artificial Intelligence and Machine Learning development, Proficiency in written and oral communication in English. Place of employment: Karlskrona
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, e.g git. Experience of Linux/UNIX-terminal. Excellent communication- and collaboration skills. You speak and write English fluently. Meriting Experience of HPC or corresponding computer systems
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with our research team, you will contribute to projects involving data integration, advanced analytics, and machine learning. While the projects are primarily computational, they are closely connected
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. Methods from probability theory, communication theory, electromagnetics, optimization and machine learning will play an important role. We are ultimately looking to either one of the two broad research