316 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" research jobs in Sweden
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to contribute to a positive work environment. We also value the ability to work independently in carrying out work tasks, as well as openness to learning new skills and taking on new responsibilities. We value
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testing and collaboration with infrastructure owners or managers - Experience in supervision - Knowledge of data-driven methods, signal processing, or machine learning - Familiarity with sustainable
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–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience collaborating in interdisciplinary research teams A doctoral
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from Hi-C and Capture Hi-C experiments. Have experience developing graphical user interfaces (GUIs). Candidates with knowledge or experience in machine learning methods will be prioritized. Successful
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, and stimulating environment. We value communication and collaboration and a workplace that promotes learning and development for all employees. We are also committed to building a safe and positive
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in English. Mark your application with the reference number below. Closing date for application: 13 April 2026 Reference number: 2464-2026 URL to this page https://web103.reachmee.com/ext/I003/583/main
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. The position includes the opportunity for three weeks of training in higher education teaching and learning. The purpose of the position is to develop the independence as a researcher and to create
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of training in higher education teaching and learning. The purpose of the position is to develop the independence as a researcher and to create the opportunity of further development. Your work duties will
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network modelling and machine learning for regulatory inference. - Functional validation of candidate TE‑CREs in spruce using UPSC transformation and somatic embryogenesis pipelines; evaluating drought
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projects in data-driven nutrition, such as: statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and health data analysis of omics data (metabolomics, proteomics, microbiome